BEI and equity paper pre-analyses
B. Thierry, Spherelab
10 December, 2021
1 Proposal
1.1 Original grant proposal for Census tract level analyses
1.1.1 Objective 1: Socio-spatial inequalities in urban interventions:
- To test if urban interventions tend to be located in low SES neighborhoods (H1a); we will run the following model: \[Log(𝔼(UI_{𝑖} \mid X_{𝑖})) = \alpha + \beta_{1} * SES_{𝑖}\] a Poisson regression model with \(UI\) = the number of Urban Interventions between 2016 and 2021, \(SES\) = the socio-economic status of the census track at 2016.
- To test the reduction in socio-economic inequalities in urban conditions (e.g. length and type of bicycle lanes, NDVI) between 2006 and 2020 (H1b), we will run the following model: \[𝔼(UC_{ij} \mid X_{ij}) = \beta_{0j} + \beta_{1j} ∗ SES + \beta_{2j} ∗ Time + \beta_{3j} ∗ SES ∗ Time + \epsilon_{ij}\] a multilevel linear model with \(UC\) = the Urban condition, \(SES\) = the socio-economic status of the census track.
1.1.2 Objective 2: Examining causal pathways and directionality of intervention/gentrification relation:
- To test if urban interventions occur before gentrification or if gentrification occurs before the implementation of new urban interventions, we will run two models : (1) \[Logit(𝔼(G_{i} \mid X_{i})) = \alpha + \beta_{1} ∗ UI_{i} + + \beta_{2} ∗ PG_{i} + \beta ∗ X_{control_{i}}\] a logistic regression model among low SES census tracts (2006) with \(G\) = Gentrification between 2006 and 2016, \(UI\) = the number of Urban Interventions between 2001 and 2006, and \(PG\) = SES status 2006; and (2) \[Logit(𝔼(UI_{i} \mid X_{i})) = \alpha + \beta_{1} ∗ G_{i} + \beta_{2} ∗ UC_{i} + \beta ∗ X_{control_{i}}\] a Poisson regression model, with \(UI\) = the number of Urban Interventions between 2016 and 2023, \(G\) = Gentrification between 2006 and 2016, and \(UC\) = Urban Conditions in 2016.
1.2 Paper objective
We focus on obj #1: are urban interventions tend to be located in low SES neighborhoods. Here, the urban interventions considered are bike lanes and canopy/tree coverage and low SES ~ high Pampalon deprivation index. In a second step, we will look at the variations of UI and SES and their association.
Data extraction and pre-analyses for the paper looking at BEI and equity. BEI comprise bike lanes and canopy changes while equity is measured through Pampalon deprivation index. (Partially adapted from original work on BEI bike lanes – see bike_lane_stats.R and ReadMe.md.)
Paper is available here.
General processing steps:
- Get CT 2016 boundaries (from Cancensus API)
- Get BEI interventions (canopy / bike lanes) for the selected years (2011 / 2015 / 2016 / 2019)
- Compute changes between years
- for the original CT boundaries
- for buffered CTs (250 / 500 / 750m)
In a second phase, these BEI changes will be linked to Pampalon index for 2016 (and 2011 ?)
UPDATE 2021-12-02 Following discussion with @Yan, add normalized bike line changes:
- by CT/buffer area
- by street length within CT/buffer
UPDATE 2021-12-08 Following discussion with @Yan
- Keep only normalized variation of bike lane length
- Reverse relation: BEI metric = f(Equity metric (Pampalon / gentrification))
- Display association at baseline, then add delta over time
- Canopy : distinguish between high and low canopy
2 Built Environment Intervention Extraction
2.1 Bike lane changes
We use data categorized by Philippe Apparicio’s team who manually identified bike lanes for each census year since 1991. For this study, we limit ourselves to 2016 and 2011 census years.
On top of the original CT boundaries, three levels of buffer have been applied to the CT – 250m, 500m & 750m. Then the same series of processing steps (see above) have been applied to the buffers.
# Bike lanes, from Ph. Apparicio
reseau <- st_read(dsn="data/ReseauCyclableFinal.gdb", layer = "Reseau") # Already in NAD83 / MTM zone 8## Reading layer `Reseau' from data source
## `/Users/benoit/WORKSPACE/gentrification_BEI_equity/data/ReseauCyclableFinal.gdb'
## using driver `OpenFileGDB'
## Simple feature collection with 82166 features and 72 fields
## Geometry type: GEOMETRY
## Dimension: XYZ, XYZM
## Bounding box: xmin: 266985.5 ymin: 5029251 xmax: 320986.1 ymax: 5062652
## z_range: zmin: 0 zmax: 43
## m_range: mmin: 0 mmax: 43
## Projected CRS: NAD83 / MTM zone 8bike_lane <- reseau %>%
filter(An2016 == 1 | An2011 == 1) %>%
select(IdRte, ClsRte, Zone, starts_with("An"), starts_with("Typo_")) %>%
st_cast("MULTILINESTRING") # Get rid of a few MULTICURVE geometries
# CT boundaries for Montreal
CT16 <- get_census(dataset='CA16', regions=list(CMA='24462'), level='CT', geo_format = "sf") %>%
filter(Type == "CT") %>%
mutate(interact_aoi = CD_UID %in% c(2466, 2465, 2458)) %>% # Flag Montréal island, Laval and the South shore (Longueuil, St-Lambert, Brossard)
st_transform(st_crs(bike_lane))## Reading geo data from local cache.CT11 <- get_census(dataset='CA11', regions=list(CMA='24462'), level='CT', geo_format = "sf") %>%
filter(Type == "CT") %>%
st_transform(st_crs(bike_lane))## Reading geo data from local cache.compute_bikelane_by_area <- function(sf_areas, year_fld, typo_fld) {
# Compute length of bike lanes within each area.
# --
# Parameters:
# - sf_areas: sf class object defining the areas of interest, must have a GeoUID field
# - year_fld: field name specifying the year of interest, e.g. An2016
# - typo_fld: field name specifying the typology for the year of interest, e.g. Typo_2016
year_fld <- enquo(year_fld)
typo_fld <- enquo(typo_fld)
# Compute intersection of bike lanes with areas
bk <- bike_lane %>%
filter(!!year_fld == 1) %>%
st_intersection(sf_areas) %>%
mutate(bike_lane_length = st_length(.)) %>%
as.data.frame() %>%
group_by(GeoUID, !!typo_fld) %>%
summarise(bike_lane_length = sum(bike_lane_length)) %>%
ungroup() %>%
pivot_wider(names_from = !!typo_fld, names_prefix = "Bike_class", names_sort = TRUE,
values_from = bike_lane_length, values_fill = units::set_units(0, m)) %>%
mutate(Bike_lane_total = units::set_units(rowSums(select(., starts_with("Bike_class"))), m))
# Merge back into original sf_areas
bk <- sf_areas %>%
left_join(bk)
# Replace NA by 0, which occur in Bike_class length
bk[is.na(bk)] <- 0
return(bk)
}
compute_streetlength_by_area <- function(sf_areas) {
# Compute length of streets within each area.
# --
# Parameters:
# - sf_areas: sf class object defining the areas of interest, must have a GeoUID field
# Compute intersection of streets with areas
bk <- reseau %>%
st_cast("MULTILINESTRING") %>% # Get rid of a few MULTICURVE geometries
st_intersection(sf_areas) %>%
mutate(street_length = st_length(.)) %>%
as.data.frame() %>%
group_by(GeoUID) %>%
summarise(street_length = sum(street_length)) %>%
ungroup()
# Merge back into original sf_areas
bk <- sf_areas %>%
mutate(shape_area_km2 = units::set_units(st_area(.), 'km^2')) %>%
left_join(bk)
# Replace NA by 0
bk[is.na(bk)] <- 0
return(bk)
}
# Compute year 2016 and year 2011 bike lanes within 2016 CTs
# NB: contrary to the original work, we keep the same area of reference, i.e. 2016
bike_lane_by_CT16 <- compute_bikelane_by_area(CT16, An2016, Typo_2016)
bike_lane_by_CT11 <- compute_bikelane_by_area(CT16, An2011, Typo_2011)
# Compute buffers, with 3 radii and for each census year
radii <- c(250, 500, 750)
buf_CT16 <- lapply(radii, st_buffer, x=CT16)
names(buf_CT16) <- lapply(radii, function(b) {paste0("buf", b)})
# Compute bike length for each buffer/census year
buf_CT16_w_bike_length <- lapply(buf_CT16, compute_bikelane_by_area, year_fld=An2016, typo_fld=Typo_2016)
names(buf_CT16_w_bike_length) <- lapply(radii, function(b) {paste0("buf", b)})
buf_CT16_w_bike_length$original <- bike_lane_by_CT16
buf_CT11_w_bike_length <- lapply(buf_CT16, compute_bikelane_by_area, year_fld=An2011, typo_fld=Typo_2011)
names(buf_CT11_w_bike_length) <- lapply(radii, function(b) {paste0("buf", b)})
buf_CT11_w_bike_length$original <- bike_lane_by_CT11
# Compute total street length within CT/buffer
street_length_by_CT16 <- compute_streetlength_by_area(CT16)
buf_CT16_street_length <- lapply(buf_CT16, compute_streetlength_by_area)
names(buf_CT16_street_length) <- lapply(radii, function(b) {paste0("buf", b)})
buf_CT16_street_length$original <- street_length_by_CT16
# Reorganize data to have all data in one dataframe
bike_lane_changes <- CT16 %>%
left_join(select(as.data.frame(buf_CT16_street_length$original), GeoUID, street_length, shape_area_km2), by="GeoUID") %>%
left_join(select(as.data.frame(buf_CT16_street_length$buf250), GeoUID, street_length, shape_area_km2), by="GeoUID", suffix=c(".ct", ".b250")) %>%
left_join(select(as.data.frame(buf_CT16_street_length$buf500), GeoUID, street_length, shape_area_km2), by="GeoUID") %>%
left_join(select(as.data.frame(buf_CT16_street_length$buf750), GeoUID, street_length, shape_area_km2), by="GeoUID", suffix=c(".b500", ".b750")) %>%
left_join(select(as.data.frame(buf_CT16_w_bike_length$original), GeoUID, starts_with("Bike_")), by="GeoUID") %>%
left_join(select(as.data.frame(buf_CT11_w_bike_length$original), GeoUID, starts_with("Bike_")), by="GeoUID", suffix=c(".2016ct", ".2011ct")) %>%
left_join(select(as.data.frame(buf_CT16_w_bike_length$buf250), GeoUID, starts_with("Bike_")), by="GeoUID") %>%
left_join(select(as.data.frame(buf_CT11_w_bike_length$buf250), GeoUID, starts_with("Bike_")), by="GeoUID", suffix=c(".2016b250", ".2011b250")) %>%
left_join(select(as.data.frame(buf_CT16_w_bike_length$buf500), GeoUID, starts_with("Bike_")), by="GeoUID") %>%
left_join(select(as.data.frame(buf_CT11_w_bike_length$buf500), GeoUID, starts_with("Bike_")), by="GeoUID", suffix=c(".2016b500", ".2011b500")) %>%
left_join(select(as.data.frame(buf_CT16_w_bike_length$buf750), GeoUID, starts_with("Bike_")), by="GeoUID") %>%
left_join(select(as.data.frame(buf_CT11_w_bike_length$buf750), GeoUID, starts_with("Bike_")), by="GeoUID", suffix=c(".2016b750", ".2011b750"))
# Compute ratio of bike lane vs street length
bike_lane_changes <- bike_lane_changes %>%
mutate(Bike_lane.by.street.2011ct = 100 * Bike_lane_total.2011ct / street_length.ct,
Bike_lane.by.street.2011b250 = 100 * Bike_lane_total.2011b250 / street_length.b250,
Bike_lane.by.street.2011b500 = 100 * Bike_lane_total.2011b500 / street_length.b500,
Bike_lane.by.street.2011b750 = 100 * Bike_lane_total.2011b750 / street_length.b750,
Bike_lane.by.street.2016ct = 100 * Bike_lane_total.2016ct / street_length.ct,
Bike_lane.by.street.2016b250 = 100 * Bike_lane_total.2016b250 / street_length.b250,
Bike_lane.by.street.2016b500 = 100 * Bike_lane_total.2016b500 / street_length.b500,
Bike_lane.by.street.2016b750 = 100 * Bike_lane_total.2016b750 / street_length.b750)
# Compute change between 2011 and 2016 (only for total bike lane length)
bike_lane_changes <- bike_lane_changes %>%
mutate(Bike_lane_diff.2011.2016ct = Bike_lane_total.2016ct - Bike_lane_total.2011ct,
Bike_lane_diff.2011.2016b250 = Bike_lane_total.2016b250 - Bike_lane_total.2011b250,
Bike_lane_diff.2011.2016b500 = Bike_lane_total.2016b500 - Bike_lane_total.2011b500,
Bike_lane_diff.2011.2016b750 = Bike_lane_total.2016b750 - Bike_lane_total.2011b750)
# Normalize bike lane change by (i) street length and (ii) area
bike_lane_changes <- bike_lane_changes %>%
mutate(Bike_lane_diff.by.street.2011.2016ct = 100 * Bike_lane_diff.2011.2016ct / street_length.ct,
Bike_lane_diff.by.street.2011.2016b250 = 100 * Bike_lane_diff.2011.2016b250 / street_length.b250,
Bike_lane_diff.by.street.2011.2016b500 = 100 * Bike_lane_diff.2011.2016b500 / street_length.b500,
Bike_lane_diff.by.street.2011.2016b750 = 100 * Bike_lane_diff.2011.2016b750 / street_length.b750,
Bike_lane_diff.by.area.2011.2016ct = Bike_lane_diff.2011.2016ct / shape_area_km2.ct,
Bike_lane_diff.by.area.2011.2016b250 = Bike_lane_diff.2011.2016b250 / shape_area_km2.b250,
Bike_lane_diff.by.area.2011.2016b500 = Bike_lane_diff.2011.2016b500 / shape_area_km2.b500,
Bike_lane_diff.by.area.2011.2016b750 = Bike_lane_diff.2011.2016b750 / shape_area_km2.b750)
# Save results
st_write(bike_lane_changes, dsn = "data/bike_length_changes.gpkg", delete_layer = TRUE)## Deleting layer `bike_length_changes' using driver `GPKG'
## Writing layer `bike_length_changes' to data source
## `data/bike_length_changes.gpkg' using driver `GPKG'
## Writing 970 features with 104 fields and geometry type Multi Polygon.# Clean up
rm(buf_CT16)
rm(bike_lane_by_CT11, bike_lane_by_CT16)
rm(buf_CT11_w_bike_length, buf_CT16_w_bike_length)
rm(street_length_by_CT16, buf_CT16_street_length)2.1.1 Illustration of bike lane metrics
Check output for one specific dataset (Census tracts 2016, no buffer)
2.1.1.1 Raw length
Bike lane length in 2016 within CTs, measured in meters
ggplot() +
geom_sf(data=filter(bike_lane_changes, interact_aoi), mapping = aes(fill=as.numeric(Bike_lane_total.2016ct)), lwd=0) +
scale_fill_continuous(name = "Total length (m)")+
labs(title = "Length of bike lanes within 2016 CTs", subtitle = "(INTERACT study area || for control only)")
2.1.1.2 Absolute length change
Absolute bike lane length change between 2011 and 2016, in meters
ggplot() +
geom_sf(data=filter(bike_lane_changes, interact_aoi), mapping = aes(fill=as.numeric(Bike_lane_diff.2011.2016ct)), lwd=0) +
scale_fill_gradient2(name = "Length (m)")+
labs(title = "Changes in bike lane between 2011 and 2016", subtitle = "(INTERACT study area || CT level || for control only)")
2.1.1.3 Normalized length change (by street)
Relative bike lane length change between 2011 and 2016, normalized by street length within CT in 2016, expressed in %
\[Variation = \frac{Bike Lane_{2016}}{Street Length_{2016}} - \frac{Bike Lane_{2011}}{Street Length_{2016}}\]
ggplot() +
geom_sf(data=filter(bike_lane_changes, interact_aoi), mapping = aes(fill=as.numeric(Bike_lane_diff.by.street.2011.2016ct)), lwd=0) +
scale_fill_gradient2(name = "Variation (%)")+
labs(title = "Changes in bike lane between 2011 and 2016, normalized by street", subtitle = "(INTERACT study area || CT level || for control only)")
2.1.1.4 Normalized length change (by area)
Relative bike lane length change between 2011 and 2016, normalized by CT area, expressed in \(\frac{km}{km^{2}}\)
\[Ratio = \frac{Bike Lane_{2016}}{CT Area_{2016}} - \frac{Bike Lane_{2011}}{CT Area_{2016}}\]
ggplot() +
geom_sf(data=filter(bike_lane_changes, interact_aoi), mapping = aes(fill=as.numeric(Bike_lane_diff.by.area.2011.2016ct)), lwd=0) +
scale_fill_gradient2(name = "Ratio (1/km)")+
labs(title = "Changes in bike lane between 2011 and 2016, normalized by area", subtitle = "(INTERACT study area || CT level || for control only)")
2.1.1.5 Normalized length change (by street) NEW
After some discussion with Yan, we envision using a relative bike lane length ratio change between 2011 and 2016, normalized by the ratio in 2011, expressed in %
\[Variation = \frac{\frac{Bike Lane_{2016}}{Street Length_{2016}} - \frac{Bike Lane_{2011}}{Street Length_{2016}}}{\frac{Bike Lane_{2011}}{Street Length_{2016}}} = \frac{Bike Lane_{2016} - Bike Lane_{2011}}{Bike Lane_{2011}}\]
Problem: all CT with no bike lane in 2011 get a missing data, contrary to the original metric, which measured the absolute variation of the ratio of bike lane to street length.
.bike_lane_changes <- bike_lane_changes %>%
mutate(Bike_lane_diff.by.street.relative.2011.2016ct = Bike_lane_diff.by.street.2011.2016ct / (Bike_lane_total.2011ct / street_length.ct))
ggplot() +
geom_sf(data=filter(.bike_lane_changes, interact_aoi), mapping = aes(fill=as.numeric(Bike_lane_diff.by.street.relative.2011.2016ct)), lwd=0) +
scale_fill_gradient2(name = "Ratio")+
labs(title = "Changes in bike lane ratio between 2011 and 2016, normalized by ratio in 2011", subtitle = "(INTERACT study area || CT level || for control only)")
2.1.2 Basic stats on each layer
2.1.2.1 Census Tracts
# CT level
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.2011.2016ct)), binwidth = 250) +
xlab("Difference of bike lane length between 2016 and 2011 | CT level")
summary(bike_lane_changes$Bike_lane_diff.2011.2016ct)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1377.7 0.0 0.0 262.1 189.7 14463.8ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.street.2011.2016ct))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by street")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 252 rows containing non-finite values (stat_bin).
summary(bike_lane_changes$Bike_lane_diff.by.street.2011.2016ct)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -100.000 0.000 0.000 3.665 4.496 100.000 252ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.area.2011.2016ct))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by area")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
summary(bike_lane_changes$Bike_lane_diff.by.area.2011.2016ct)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.1474 0.0000 0.0000 0.4237 0.1764 8.9852ggplot(.bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.street.relative.2011.2016ct))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by ratio in 2011")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 461 rows containing non-finite values (stat_bin).
summary(.bike_lane_changes$Bike_lane_diff.by.street.relative.2011.2016ct)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -100.0000 0.0000 0.2199 Inf 102.7796 Inf 3672.1.2.2 Buffers 250m
# buf250 level
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.2011.2016b250)), binwidth = 250) +
xlab("Difference of bike lane length between 2016 and 2011 | buf 250m")
summary(bike_lane_changes$Bike_lane_diff.2011.2016b250)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3452.5 0.0 0.0 646.7 933.8 20786.8ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.street.2011.2016b250))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by street")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 233 rows containing non-finite values (stat_bin).
summary(bike_lane_changes$Bike_lane_diff.by.street.2011.2016b250)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -38.5802 0.0000 0.8538 3.3168 4.9043 56.6588 233ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.area.2011.2016b250))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by area")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
summary(bike_lane_changes$Bike_lane_diff.by.area.2011.2016b250)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.4622 0.0000 0.0000 0.4001 0.4306 6.21562.1.2.3 Buffers 500m
# buf500 level
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.2011.2016b500)), binwidth = 250) +
xlab("Difference of bike lane length between 2016 and 2011 | buf 500m")
summary(bike_lane_changes$Bike_lane_diff.2011.2016b250)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3452.5 0.0 0.0 646.7 933.8 20786.8ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.street.2011.2016b500))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by street")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 229 rows containing non-finite values (stat_bin).
summary(bike_lane_changes$Bike_lane_diff.by.street.2011.2016b500)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -26.891 0.000 1.385 3.298 5.323 22.121 229ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.area.2011.2016b500))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by area")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
summary(bike_lane_changes$Bike_lane_diff.by.area.2011.2016b500)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.94397 0.00000 0.03393 0.38745 0.49370 4.004172.1.2.4 Buffers 750m
# buf750 level
ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.2011.2016b750)), binwidth = 250) +
xlab("Difference of bike lane length between 2016 and 2011 | buf 750m")
summary(bike_lane_changes$Bike_lane_diff.2011.2016b750)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3697.5 0.0 578.6 1842.2 2928.6 29325.1ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.street.2011.2016b750))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by street")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 222 rows containing non-finite values (stat_bin).
summary(bike_lane_changes$Bike_lane_diff.by.street.2011.2016b750)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## -4.7579 0.1418 1.7793 3.2961 5.2054 22.5448 222ggplot(bike_lane_changes) +
geom_histogram(aes(as.numeric(Bike_lane_diff.by.area.2011.2016b750))) +
xlab("Difference of bike lane length between 2016 and 2011, normalized by area")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
summary(bike_lane_changes$Bike_lane_diff.by.area.2011.2016b750)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -0.59760 0.00000 0.07793 0.38166 0.53774 3.954632.2 Canopy changes
Canopy changes is based on data produced by CMM, using multispectral aerial imagery and lidar. In order to sync the observations with the census years, we focus on 2011 and 2019 with one extra observation point in 2015.
The processing steps are similar to the ones for the bike lanes:
- Import the raster for each of the 3 years
- Compute proportion of canopy within each area level (CT and buffers) for the 3 years
# Codes du raster "espace vert"
# 0. No data (hors CMM)
# 1. NDVI < 0,3 et MNH < 3,0m = Minéral bas (route, stationnement, etc.)
# 2. NDVI < 0,3 et MNH ≥ 3,0m = Minéral haut (constructions)
# 3. NDVI ≥ 0,3 et MNH < 3,0m = Végétal bas (culture, gazon, etc.)
# 4. NDVI ≥ 0,3 et MNH ≥ 3,0m = Végétal haut (canopée)
# 5. Aquatique
# Load rasters into pg database for further processing
system('psql -d xgentrif_bei -c "CREATE EXTENSION IF NOT EXISTS postgis"')
system('psql -d xgentrif_bei -c "CREATE EXTENSION IF NOT EXISTS postgis_raster"')
if (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2019') IS NOT NULL;")) == 0) {
system("raster2pgsql -s 32188 -I -C -M data/canopy/2019/*.tif -F -t 1000x1000 canopee2019 | psql -d xgentrif_bei", intern = TRUE)
} else { message("PG Raster 'canopee2019' already imported") }## PG Raster 'canopee2019' already importedif (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2017') IS NOT NULL;")) == 0) {
system("raster2pgsql -s 32188 -I -C -M data/canopy/2017/*.tif -F -t 1000x1000 canopee2017 | psql -d xgentrif_bei", intern = TRUE)
} else { message("PG Raster 'canopee2017' already imported") }## PG Raster 'canopee2017' already importedif (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2015') IS NOT NULL;")) == 0) {
system("raster2pgsql -s 32188 -I -C -M data/canopy/2015/*.tif -F -t 1000x1000 canopee2015 | psql -d xgentrif_bei", intern = TRUE)
} else { message("PG Raster 'canopee2015' already imported") }## PG Raster 'canopee2015' already importedif (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2011') IS NOT NULL;")) == 0) {
system("raster2pgsql -s 32188 -I -C -M data/canopy/2011/*.tif -F -t 1000x1000 canopee2011 | psql -d xgentrif_bei", intern = TRUE)
} else { message("PG Raster 'canopee2011' already imported") }## PG Raster 'canopee2011' already imported# Resample to 10m as the original rasters have a 1m resolution, which is too high to allow for a swift processing
if (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2019_10m') IS NOT NULL;")) == 0) {
system("gdal_translate -of GTiff PG:\"host=localhost dbname=xgentrif_bei table=canopee2019 mode=2\" -r mode -tr 10 10 data/canopy/canopee2019_10m.tif")
system("raster2pgsql -s 32188 -I -C -M data/canopy/canopee2019_10m.tif -F -t 100x100 canopee2019_10m | psql -d xgentrif_bei")
} else { message("PG Raster 'canopee2019_10m' already imported") }## PG Raster 'canopee2019_10m' already importedif (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2017_10m') IS NOT NULL;")) == 0) {
system("gdal_translate -of GTiff PG:\"host=localhost dbname=xgentrif_bei table=canopee2017 mode=2\" -r mode -tr 10 10 data/canopy/canopee2017_10m.tif")
system("raster2pgsql -s 32188 -I -C -M data/canopy/canopee2017_10m.tif -F -t 100x100 canopee2017_10m | psql -d xgentrif_bei")
} else { message("PG Raster 'canopee2017_10m' already imported") }## PG Raster 'canopee2017_10m' already importedif (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2015_10m') IS NOT NULL;")) == 0) {
system("gdal_translate -of GTiff PG:\"host=localhost dbname=xgentrif_bei table=canopee2015 mode=2\" -r mode -tr 10 10 data/canopy/canopee2015_10m.tif")
system("raster2pgsql -s 32188 -I -C -M data/canopy/canopee2015_10m.tif -F -t 100x100 canopee2015_10m | psql -d xgentrif_bei")
} else { message("PG Raster 'canopee2015_10m' already imported") }## PG Raster 'canopee2015_10m' already importedif (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('canopee2011_10m') IS NOT NULL;")) == 0) {
system("gdal_translate -of GTiff PG:\"host=localhost dbname=xgentrif_bei table=canopee2011 mode=2\" -r mode -tr 10 10 data/canopy/canopee2011_10m.tif")
system("raster2pgsql -s 32188 -I -C -M data/canopy/canopee2011_10m.tif -F -t 100x100 canopee2011_10m | psql -d xgentrif_bei")
} else { message("PG Raster 'canopee2011_10m' already imported") }## PG Raster 'canopee2011_10m' already imported# Push CT16 to pg
if (nrow(dbGetQuery(con_bei, "SELECT 1 test WHERE to_regclass('ct16') IS NOT NULL;")) == 0) {
CT16 %>%
st_transform(crs = 32188) %>%
st_write(con_bei, "ct16",
layer_options = c("OVERWRITE=yes", "LAUNDER=true", "SPATIAL_INDEX=gist", "GEOMETRY_NAME=geom"))
system("psql -d xgentrif_bei -c 'CREATE INDEX ON ct16 USING gist (geometry)'")
} else { message("PG Layer CT16 already imported") }## PG Layer CT16 already imported2.2.1 Extract % of green spaces at various scales
UPDATE 2021-12-08: following discussion with Yan, we decide to focus on extracting canopy years in sync with census years, i.e. 2011 and 2017, and 2015 as an intermediary year. (Previously, we were using the last available year, i.e. 2019), plus keep the option of looking high/low canopy separately
2.2.1.1 Census Tracts
WITH cnt19 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2019_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee19 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2019
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2019
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2019
FROM cnt19
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
),
cnt17 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2017_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee17 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2017
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2017
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2017
FROM cnt17
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
),
cnt15 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2015_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee15 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2015
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2015
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2015
FROM cnt15
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
),
cnt11 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2011_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee11 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2011
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2011
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2011
FROM cnt11
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
)
SELECT "GeoUID"
,st_area(geometry) ct_area_m2
,coalesce(pct_esp_vert_low_2011, 0) pct_esp_vert_low_2011
,coalesce(pct_esp_vert_high_2011, 0) pct_esp_vert_high_2011
,coalesce(pct_esp_vert_2011, 0) pct_esp_vert_2011
,coalesce(pct_esp_vert_low_2015, 0) pct_esp_vert_low_2015
,coalesce(pct_esp_vert_high_2015, 0) pct_esp_vert_high_2015
,coalesce(pct_esp_vert_2015, 0) pct_esp_vert_2015
,coalesce(pct_esp_vert_low_2017, 0) pct_esp_vert_low_2017
,coalesce(pct_esp_vert_high_2017, 0) pct_esp_vert_high_2017
,coalesce(pct_esp_vert_2017, 0) pct_esp_vert_2017
,coalesce(pct_esp_vert_low_2019, 0) pct_esp_vert_low_2019
,coalesce(pct_esp_vert_high_2019, 0) pct_esp_vert_high_2019
,coalesce(pct_esp_vert_2019, 0) pct_esp_vert_2019
FROM ct16
FULL JOIN canopee19 USING ("GeoUID")
FULL JOIN canopee17 USING ("GeoUID")
FULL JOIN canopee15 USING ("GeoUID")
FULL JOIN canopee11 USING ("GeoUID");
2.2.1.2 Buffers 250m
WITH ct16 AS (
select "GeoUID", ST_Buffer(geometry, 250) geometry
from ct16
),
cnt19 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2019_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee19 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2019
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2019
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2019
FROM cnt19
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
),
cnt17 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2017_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee17 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2017
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2017
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2017
FROM cnt17
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
),
cnt15 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2015_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee15 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2015
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2015
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2015
FROM cnt15
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
),
cnt11 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2011_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee11 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2011
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2011
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2011
FROM cnt11
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
)
SELECT "GeoUID"
,st_area(geometry) ct_area_m2
,coalesce(pct_esp_vert_low_2011, 0) pct_esp_vert_low_2011
,coalesce(pct_esp_vert_high_2011, 0) pct_esp_vert_high_2011
,coalesce(pct_esp_vert_2011, 0) pct_esp_vert_2011
,coalesce(pct_esp_vert_low_2015, 0) pct_esp_vert_low_2015
,coalesce(pct_esp_vert_high_2015, 0) pct_esp_vert_high_2015
,coalesce(pct_esp_vert_2015, 0) pct_esp_vert_2015
,coalesce(pct_esp_vert_low_2017, 0) pct_esp_vert_low_2017
,coalesce(pct_esp_vert_high_2017, 0) pct_esp_vert_high_2017
,coalesce(pct_esp_vert_2017, 0) pct_esp_vert_2017
,coalesce(pct_esp_vert_low_2019, 0) pct_esp_vert_low_2019
,coalesce(pct_esp_vert_high_2019, 0) pct_esp_vert_high_2019
,coalesce(pct_esp_vert_2019, 0) pct_esp_vert_2019
FROM ct16
FULL JOIN canopee19 USING ("GeoUID")
FULL JOIN canopee17 USING ("GeoUID")
FULL JOIN canopee15 USING ("GeoUID")
FULL JOIN canopee11 USING ("GeoUID");2.2.1.3 Buffers 500m
WITH ct16 AS (
select "GeoUID", ST_Buffer(geometry, 500) geometry
from ct16
),
cnt19 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2019_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee19 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2019
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2019
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2019
FROM cnt19
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
),
cnt17 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2017_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee17 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2017
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2017
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2017
FROM cnt17
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
),
cnt15 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2015_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee15 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2015
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2015
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2015
FROM cnt15
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
),
cnt11 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2011_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee11 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2011
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2011
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2011
FROM cnt11
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
)
SELECT "GeoUID"
,st_area(geometry) ct_area_m2
,coalesce(pct_esp_vert_low_2011, 0) pct_esp_vert_low_2011
,coalesce(pct_esp_vert_high_2011, 0) pct_esp_vert_high_2011
,coalesce(pct_esp_vert_2011, 0) pct_esp_vert_2011
,coalesce(pct_esp_vert_low_2015, 0) pct_esp_vert_low_2015
,coalesce(pct_esp_vert_high_2015, 0) pct_esp_vert_high_2015
,coalesce(pct_esp_vert_2015, 0) pct_esp_vert_2015
,coalesce(pct_esp_vert_low_2017, 0) pct_esp_vert_low_2017
,coalesce(pct_esp_vert_high_2017, 0) pct_esp_vert_high_2017
,coalesce(pct_esp_vert_2017, 0) pct_esp_vert_2017
,coalesce(pct_esp_vert_low_2019, 0) pct_esp_vert_low_2019
,coalesce(pct_esp_vert_high_2019, 0) pct_esp_vert_high_2019
,coalesce(pct_esp_vert_2019, 0) pct_esp_vert_2019
FROM ct16
FULL JOIN canopee19 USING ("GeoUID")
FULL JOIN canopee17 USING ("GeoUID")
FULL JOIN canopee15 USING ("GeoUID")
FULL JOIN canopee11 USING ("GeoUID");2.2.1.4 Buffers 750m
WITH ct16 AS (
select "GeoUID", ST_Buffer(geometry, 750) geometry
from ct16
),
cnt19 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2019_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee19 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2019
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2019
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2019
FROM cnt19
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
),
cnt17 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2017_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee17 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2017
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2017
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2017
FROM cnt17
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
),
cnt15 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2015_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee15 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2015
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2015
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2015
FROM cnt15
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
),
cnt11 AS (
SELECT "GeoUID"
,(pvc).value, SUM((pvc).count) As total
FROM (SELECT "GeoUID"
,ST_ValueCount(ST_Clip(rast, geometry)) As pvc
FROM canopee2011_10m
JOIN ct16 ON ST_Intersects(geometry, rast)
) As foo
GROUP BY "GeoUID", (pvc).value
),
canopee11 AS (
SELECT "GeoUID"
,round(100. * sum(total) FILTER (WHERE value in (3, 4)) / sum(total), 2) AS pct_esp_vert_2011
,round(100. * sum(total) FILTER (WHERE value = 3) / sum(total), 2) AS pct_esp_vert_low_2011
,round(100. * sum(total) FILTER (WHERE value = 4) / sum(total), 2) AS pct_esp_vert_high_2011
FROM cnt11
WHERE value > 0 -- discard no data, including postgis raster no data
GROUP BY "GeoUID"
)
SELECT "GeoUID"
,st_area(geometry) ct_area_m2
,coalesce(pct_esp_vert_low_2011, 0) pct_esp_vert_low_2011
,coalesce(pct_esp_vert_high_2011, 0) pct_esp_vert_high_2011
,coalesce(pct_esp_vert_2011, 0) pct_esp_vert_2011
,coalesce(pct_esp_vert_low_2015, 0) pct_esp_vert_low_2015
,coalesce(pct_esp_vert_high_2015, 0) pct_esp_vert_high_2015
,coalesce(pct_esp_vert_2015, 0) pct_esp_vert_2015
,coalesce(pct_esp_vert_low_2017, 0) pct_esp_vert_low_2017
,coalesce(pct_esp_vert_high_2017, 0) pct_esp_vert_high_2017
,coalesce(pct_esp_vert_2017, 0) pct_esp_vert_2017
,coalesce(pct_esp_vert_low_2019, 0) pct_esp_vert_low_2019
,coalesce(pct_esp_vert_high_2019, 0) pct_esp_vert_high_2019
,coalesce(pct_esp_vert_2019, 0) pct_esp_vert_2019
FROM ct16
FULL JOIN canopee19 USING ("GeoUID")
FULL JOIN canopee17 USING ("GeoUID")
FULL JOIN canopee15 USING ("GeoUID")
FULL JOIN canopee11 USING ("GeoUID");2.3 Pampalon index
Get it here
pampalon <- read.xlsx("data/Canada2016Pampalon/A-MSDIData_Can2016_eng/1. EquivalenceTableCanada2016_ENG.xlsx", sheet = 2) %>%
mutate(DA = as.character(DA)) %>%
select(DA, SCOREMAT, SCORESOC)
# 2016 DA boundaries for Montreal
DA16 <- get_census(dataset='CA16', regions=list(CMA='24462'), level='DA', geo_format = "sf") %>%
filter(Type == "DA") %>%
st_transform(st_crs(bike_lane))## Reading geo data from local cache.pampalon <- DA16 %>%
inner_join(pampalon, by = c("GeoUID" = "DA")) %>%
as.data.frame()
# Get Pampalon 2006
pampalon06 <- read.xlsx("data/Canada2006Pampalon/A-MSDIData_Can2006_eng/1. CorrespondenceTable_Can2006_eng.xlsx", sheet = 2) %>%
mutate(DA = as.character(DA)) %>%
select(DA, DAPOP2006, SCOREMAT, SCORESOC)
# Get LUT DA2006 <-> DA2011 from StatCan
lut_da.1 <- read.csv("data/2011_92-156_DA_AD_txt/2011_92-156_DA_AD.txt", colClasses = "character",
header = FALSE, col.names = c("DAUID2011.ADIDU2011", "DAUID2006.ADIDU2006", "DBUID2011", "DA_rel_flag")) %>%
select(!c(DBUID2011, DA_rel_flag)) %>%
unique()
# Link Pampalon 2011 to LUT and compute weighted mean of scores of Pampalon 2011
# NB: population numbers will diverge from reality when more than one DA is merged into one DA of next census
pampalon06.11 <- pampalon06 %>%
inner_join(lut_da.1, by = c("DA" = "DAUID2006.ADIDU2006")) %>%
group_by(DAUID2011.ADIDU2011) %>%
summarise(pop2006 = sum(DAPOP2006),
SCOREMAT.06 = weighted.mean(SCOREMAT, DAPOP2006, na.rm = TRUE),
SCORESOC.06 = weighted.mean(SCORESOC, DAPOP2006, na.rm = TRUE))
# Get Pampalon 2011
pampalon11 <- read.xlsx("data/Canada2011Pampalon/A-MSDIData_Can2011_eng/1. CorrespondenceTable_Can2011_eng.xlsx", sheet = 2) %>%
mutate(DA = as.character(DA)) %>%
select(DA, DAPOP2011, SCOREMAT, SCORESOC)
# Get LUT DA2011 <-> DA2016 from StatCan
lut_da <- read.csv("data/2016_92-156_DA_AD_csv/2016_92-156_DA_AD.csv", colClasses = "character") %>%
select(!c(DBUID2016.IDIDU2016, DA_rel_flag.AD_ind_rel)) %>%
unique()
# Link Pampalon 2011 to LUT, then to Pampalon 06 and finally compute weighted mean of scores of Pampalon 2011
pampalon11.16 <- pampalon11 %>%
inner_join(lut_da, by = c("DA" = "DAUID2011.ADIDU2011")) %>%
left_join(pampalon06.11, by =c("DA" = "DAUID2011.ADIDU2011")) %>%
group_by(DAUID2016.ADIDU2016) %>%
summarise(pop2011 = sum(DAPOP2011),
SCOREMAT = weighted.mean(SCOREMAT, DAPOP2011, na.rm = TRUE),
SCORESOC = weighted.mean(SCORESOC, DAPOP2011, na.rm = TRUE),
SCOREMAT.06 = weighted.mean(SCOREMAT.06, pop2006, na.rm = TRUE),
SCORESOC.06 = weighted.mean(SCORESOC.06, pop2006, na.rm = TRUE),
pop2006 = sum(pop2006))
# Then link Pampalon 2011 to 2016
pampalon <- pampalon %>%
left_join(pampalon11.16, by = c("GeoUID" = "DAUID2016.ADIDU2016"), suffix = c(".16", ".11"))
# Aggregate at the CT level
pampalon_CT <- pampalon %>%
group_by(CT_UID) %>%
summarise(wSCOREMAT.2016 = weighted.mean(SCOREMAT.16, Population, na.rm = TRUE),
wSCORESOC.2016 = weighted.mean(SCORESOC.16, Population, na.rm = TRUE),
wSCOREMAT.2011 = weighted.mean(SCOREMAT.11, pop2011, na.rm = TRUE),
wSCORESOC.2011 = weighted.mean(SCORESOC.11, pop2011, na.rm = TRUE),
wSCOREMAT.2006 = weighted.mean(SCOREMAT.06, pop2006, na.rm = TRUE),
wSCORESOC.2006 = weighted.mean(SCORESOC.06, pop2006, na.rm = TRUE))
# Clean up
rm(lut_da, lut_da.1, pampalon11.16, pampalon06.11, pampalon11, pampalon06)
# Display map
pampalon_CT_geom <- CT16 %>%
left_join(pampalon_CT, by = c("GeoUID" = "CT_UID")) %>%
filter(interact_aoi)
pampalon_data <- bi_class(pampalon_CT_geom, x = wSCOREMAT.2016, y = wSCORESOC.2016, style = "quantile", dim = 3)## Warning in classInt::classIntervals(bins_x, n = dim, style = "quantile"): var
## has missing values, omitted in finding classes## Warning in classInt::classIntervals(bins_y, n = dim, style = "quantile"): var
## has missing values, omitted in finding classesmap <- ggplot() +
geom_sf(data = pampalon_data, mapping = aes(fill = bi_class), color = "white", size = 0.1, show.legend = FALSE) +
bi_scale_fill(pal = "DkBlue", dim = 3) +
labs(title = "Pampalon: material and social deprivation index") +
theme(panel.background = element_rect(fill = "white"),
#axis.ticks = element_blank(),
#axis.text = element_blank(),
panel.grid = element_line(color = "darkgray", size = 0.2))
legend <- bi_legend(pal = "DkBlue",
dim = 3,
xlab = "Material ",
ylab = "Social ",
size = 8)
ggdraw() +
draw_plot(map, 0, 0, 1, 1) +
draw_plot(legend, 0.1, .7, 0.2, 0.2)
2.4 Gentrification
# Load gentrified CTs, 5 year span (from repo gentrification_metrics)
ding <- list()
ding[["2016"]] <- st_read("data/gentrified_5years.gpkg", "gentrified_ding_16", quiet=TRUE) %>%
filter(cma_uid_16 == "24462") %>%
st_transform(st_crs(bike_lane))
ding[["2011"]] <- st_read("data/gentrified_5years.gpkg", "gentrified_ding_11", quiet=TRUE) %>%
filter(cma_uid_11 == "24462") %>%
st_transform(st_crs(bike_lane))
ding[["2006"]] <- st_read("data/gentrified_5years.gpkg", "gentrified_ding_06", quiet=TRUE) %>%
filter(cma_uid_06 == "24462") %>%
st_transform(st_crs(bike_lane))
ding_map <- ding[["2016"]] %>%
left_join(select(as.data.frame(CT16), GeoUID, interact_aoi), by = c("ct_uid_16" = "GeoUID")) %>%
filter(interact_aoi)
ggplot(data = ding_map) +
geom_sf(aes(fill = gentrified_2016_2011, colour=gentrifiable_2011)) +
scale_fill_manual(values = c("gray", "red", "darkgray"), name = "Gentrified in 2016") +
scale_colour_manual(values = c("darkgray", "darkred", "darkgray"), name = "Gentrifiable in 2011") +
labs(title = "Census tract gentrification status in 2016")
2.5 Build complete dataset
All variables + outcome linked at the CT level
.bike_lane_changes <- bike_lane_changes %>%
as.data.frame() %>%
select(GeoUID, ends_with("ct", ignore.case = FALSE), ends_with("b250", ignore.case = FALSE), ends_with("b500", ignore.case = FALSE), ends_with("b750", ignore.case = FALSE)) %>%
select(GeoUID, starts_with("Bike_lane")) # Drop individual category lane length
bei_df <- CT16 %>%
as.data.frame() %>%
transmute(CT_UID = GeoUID,
CD_UID = CD_UID,
CSD_UID = CSD_UID,
interact_aoi = interact_aoi,
Population = Population) %>%
left_join(pampalon_CT, by="CT_UID") %>%
left_join(.bike_lane_changes, by=c("CT_UID" = "GeoUID")) %>%
left_join(as.data.frame(esp_vert_ct), by=c("CT_UID" = "GeoUID")) %>%
left_join(as.data.frame(esp_vert_buf250), by=c("CT_UID" = "GeoUID"), suffix=c("ct", "b250")) %>%
left_join(as.data.frame(esp_vert_buf500), by=c("CT_UID" = "GeoUID")) %>%
left_join(as.data.frame(esp_vert_buf750), by=c("CT_UID" = "GeoUID"), suffix=c("b500", "b750")) %>%
left_join(select(as.data.frame(ding$`2016`), ct_uid_16, starts_with("gentrif")), by=c("CT_UID" = "ct_uid_16")) %>%
left_join(select(as.data.frame(ding$`2011`), ct_uid_11, starts_with("gentrif")), by=c("CT_UID" = "ct_uid_11")) %>%
left_join(select(as.data.frame(ding$`2006`), ct_uid_06, starts_with("gentrif")), by=c("CT_UID" = "ct_uid_06"))
head(bei_df)write.csv(bei_df, "data/_results/bei_equity.csv", na="", row.names = FALSE)Included variables:
- Census Tracts variables
CT_UID: 2016 Census Tract IDCD_UID: 2016 Census DivisionCSD_UID: 2016 Census Subdivisioninteract_aoi: Does CT belong to INTERACT study area?Population: 2016 Population within CTct_area_m2.{ct|b{250|500|750}}: Area of CT or buffer of 250, 500 or 750m radius around CT, in square metersgentrified_2016_2011: Is the CT gentrified in 2016?gentrifiable_2011: Is the CT candidate to gentrification in 2011?gentrified_2011_2006: Is the CT gentrified in 2011gentrifiable_2006: Is the CT candidate to gentrification in 2006gentrified_2006_2001: Is the CT gentrified in 2006gentrifiable_2001: Is the CT candidate to gentrification in 2001
- Pampalon’s metrics
wSCOREMAT.2016: Social deprivation index in 2016 (population weighted)wSCORESOC.2016: Material deprivation index in 2016 (population weighted)wSCOREMAT.2011: Social deprivation index in 2011 (population weighted)wSCORESOC.2011: Material deprivation index in 2011 (population weighted)
- Bike lane density
Bike_lane_total.{2016|2011}{ct|b{250|500|750}}: total length of bike lanes, in 2016 or 2011, within CT or buffer of 250, 500 or 750m radiusBike_lane.by.street.{2016|2011}{ct|b{250|500|750}}: % of bike lanes compared to streets, in 2016 or 2011, within CT or buffer of 250, 500 or 750m radiusBike_lane_diff.2011.2016{ct|b{250|500|750}}: change in total length of bike lanes between 2011 and 2011, within CT or buffer of 250, 500 or 750m radiusBike_lane_diff.by.street.2011.2016{ct|b{250|500|750}}: change in total length of bike lanes between 2011 and 2011, normalized by street length, within CT or buffer of 250, 500 or 750m radiusBike_lane_diff.by.area.2011.2016{ct|b{250|500|750}}: change in total length of bike lanes between 2011 and 2011, normalized by area, within CT or buffer of 250, 500 or 750m radius
- Green spaces
pct_esp_vert_{2011|2015|2019}.{ct|b{250|500|750}}: % of green space in 2011, 2015 or 2019 within CT or buffer of 250, 500 or 750m radiuspct_esp_vert_{low|high}_{2011|2015|2019}.{ct|b{250|500|750}}: same as above, except for grass (low) and tree (high)pct_esp_vert_diff{2011|2015}.{2015|2019}.{ct|b{250|500|750}}: change in % of green space between 2011 and 2015, 2011 and 2019 as well as 2011 and 2019, within CT or buffer of 250, 500 or 750m radius
3 Preliminary analyses
3.1 Whole CMA
3.1.1 SES variable distribution
Whole area ~ Montréal CMA / CMM
.bei_df_long <- bei_df %>%
units::drop_units() %>%
select(CT_UID, CD_UID, starts_with("wSCORE")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name, scales = "free")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 122 rows containing non-finite values (stat_bin).
.bei_df_long <- bei_df %>%
units::drop_units() %>%
select(CT_UID, CD_UID, starts_with("gentrif")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_bar() +
facet_wrap(~name, scales = "free", ncol = 3)
3.1.2 BEI variable distributions
3.1.2.1 Census Tracts
.bei_df_long <- bei_df %>%
units::drop_units() %>%
select(CT_UID, CD_UID, matches("^Bike_lane.*ct$"), matches("^pct_esp_vert_diff.*ct$")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name, scales = "free", ncol = 3)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 756 rows containing non-finite values (stat_bin).
3.1.2.2 Buffers 250m
.bei_df_long <- bei_df %>%
units::drop_units() %>%
select(CT_UID, CD_UID, matches("^Bike_lane.*b250$"), matches("^pct_esp_vert_diff.*b250$")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name, scales = "free", ncol = 3)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 699 rows containing non-finite values (stat_bin).
3.1.2.3 Buffers 500m
.bei_df_long <- bei_df %>%
units::drop_units() %>%
select(CT_UID, CD_UID, matches("^Bike_lane.*b500$"), matches("^pct_esp_vert_diff.*b500$")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name, scales = "free", ncol = 3)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 687 rows containing non-finite values (stat_bin).
3.1.2.4 Buffers 750m
.bei_df_long <- bei_df %>%
units::drop_units() %>%
select(CT_UID, CD_UID, matches("^Bike_lane.*b750$"), matches("^pct_esp_vert_diff.*b750$")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name, scales = "free", ncol = 3)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 666 rows containing non-finite values (stat_bin).
3.2 INTERACT study area
INTERACT study area ~ Montréal, Laval, Longueuil, Brossard, St-Lambert
3.2.1 SES variable distribution
.bei_df_long <- bei_df %>%
filter(interact_aoi) %>%
units::drop_units() %>%
select(CT_UID, CD_UID, starts_with("wSCORE")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name, scales = "free")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 86 rows containing non-finite values (stat_bin).
.bei_df_long <- bei_df %>%
filter(interact_aoi) %>%
units::drop_units() %>%
select(CT_UID, CD_UID, starts_with("gentrif")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_bar() +
facet_wrap(~name, scales = "free", ncol = 3)
3.2.2 BEI variable distributions
3.2.2.1 Census Tracts
.bei_df_long <- bei_df %>%
filter(interact_aoi) %>%
units::drop_units() %>%
select(CT_UID, CD_UID, matches("^Bike_lane.*ct$"), matches("^pct_esp_vert_diff.*ct$")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name, scales = "free", ncol = 3)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
3.2.2.2 Buffers 250m
.bei_df_long <- bei_df %>%
filter(interact_aoi) %>%
units::drop_units() %>%
select(CT_UID, CD_UID, matches("^Bike_lane.*b250$"), matches("^pct_esp_vert_diff.*b250$")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name, scales = "free", ncol = 3)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
3.2.2.3 Buffers 500m
.bei_df_long <- bei_df %>%
filter(interact_aoi) %>%
units::drop_units() %>%
select(CT_UID, CD_UID, matches("^Bike_lane.*b500$"), matches("^pct_esp_vert_diff.*b500$")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name, scales = "free", ncol = 3)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
3.2.2.4 Buffers 750m
.bei_df_long <- bei_df %>%
filter(interact_aoi) %>%
units::drop_units() %>%
select(CT_UID, CD_UID, matches("^Bike_lane.*b750$"), matches("^pct_esp_vert_diff.*b750$")) %>%
pivot_longer(!c(CT_UID, CD_UID))
ggplot(.bei_df_long, aes(value)) +
geom_histogram() +
facet_wrap(~name, scales = "free", ncol = 3)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
4 Association between SES and BEI at baseline (2011)
Looking at objective #1 do urban interventions tend to be located in low SES neighborhoods?
4.1 Whole CMA
4.1.1 BEI vs Pampalon | material
4.1.1.1 Bike lane length
Bike lane ratio to streets (in %)
4.1.1.1.1 Census tract level
ggplot(units::drop_units(bei_df), aes(y=Bike_lane.by.street.2011ct, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.692 -8.223 -2.539 4.865 91.035
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.3615 0.4427 21.146 <2e-16 ***
## wSCOREMAT.2011 -27.2836 10.8382 -2.517 0.012 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.66 on 701 degrees of freedom
## (267 observations deleted due to missingness)
## Multiple R-squared: 0.008959, Adjusted R-squared: 0.007545
## F-statistic: 6.337 on 1 and 701 DF, p-value: 0.012054.1.1.1.2 Buffers 250m
ggplot(units::drop_units(bei_df), aes(y=Bike_lane.by.street.2011b250, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.679 -6.135 -1.549 3.578 91.612
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.4442 0.3569 26.464 < 2e-16 ***
## wSCOREMAT.2011 -30.6466 8.8234 -3.473 0.000545 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.531 on 720 degrees of freedom
## (248 observations deleted due to missingness)
## Multiple R-squared: 0.01648, Adjusted R-squared: 0.01511
## F-statistic: 12.06 on 1 and 720 DF, p-value: 0.00054484.1.1.1.3 Buffers 500m
ggplot(units::drop_units(bei_df), aes(y=Bike_lane.by.street.2011b500, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.863 -4.306 -1.043 3.152 58.090
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.1999 0.2589 35.529 < 2e-16 ***
## wSCOREMAT.2011 -28.7292 6.4113 -4.481 8.63e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.931 on 723 degrees of freedom
## (245 observations deleted due to missingness)
## Multiple R-squared: 0.02702, Adjusted R-squared: 0.02568
## F-statistic: 20.08 on 1 and 723 DF, p-value: 8.634e-064.1.1.1.4 Buffers 750m
ggplot(units::drop_units(bei_df), aes(y=Bike_lane.by.street.2011b750, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.543 -3.805 -0.617 2.484 54.694
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.0780 0.2233 40.655 < 2e-16 ***
## wSCOREMAT.2011 -25.5780 5.5471 -4.611 4.73e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.008 on 730 degrees of freedom
## (238 observations deleted due to missingness)
## Multiple R-squared: 0.0283, Adjusted R-squared: 0.02697
## F-statistic: 21.26 on 1 and 730 DF, p-value: 4.731e-064.1.1.2 Canopy change
Measuring canopy (i.e. greenness ~ grass & trees) ratio within CT/buffer in 2011 (in %)
4.1.1.2.1 Census tract level
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_2011ct, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -56.637 -12.862 0.022 11.451 55.361
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.6677 0.6414 64.960 < 2e-16 ***
## wSCOREMAT.2011 -134.5745 17.3456 -7.758 2.22e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 19.66 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.05976, Adjusted R-squared: 0.05877
## F-statistic: 60.19 on 1 and 947 DF, p-value: 2.22e-144.1.1.2.2 Buffer 250m
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_2011b250, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -49.361 -10.527 -1.515 9.806 52.577
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.1560 0.5888 68.202 < 2e-16 ***
## wSCOREMAT.2011 -111.3184 15.9216 -6.992 5.13e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 18.04 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.04909, Adjusted R-squared: 0.04808
## F-statistic: 48.88 on 1 and 947 DF, p-value: 5.126e-124.1.1.2.3 Buffer 500m
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_2011b500, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -48.103 -10.363 -1.419 9.521 53.004
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.1269 0.5747 69.822 < 2e-16 ***
## wSCOREMAT.2011 -102.7972 15.5411 -6.615 6.22e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.61 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.04416, Adjusted R-squared: 0.04315
## F-statistic: 43.75 on 1 and 947 DF, p-value: 6.224e-114.1.1.2.4 Buffer 750m
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_2011b750, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -45.887 -11.012 -1.622 9.212 59.121
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 40.2943 0.5706 70.615 < 2e-16 ***
## wSCOREMAT.2011 -95.7911 15.4305 -6.208 8.02e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.49 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.0391, Adjusted R-squared: 0.03809
## F-statistic: 38.54 on 1 and 947 DF, p-value: 8.023e-104.1.1.3 Canopy (trees) change
Measuring high canopy (i.e. trees only) ratio within CT/buffer in 2011 (in %)
4.1.1.3.1 Census tract level
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_high_2011ct, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -30.667 -6.512 -1.130 4.976 59.824
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.0646 0.3623 52.62 <2e-16 ***
## wSCOREMAT.2011 -99.5613 9.7983 -10.16 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.1 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.09831, Adjusted R-squared: 0.09736
## F-statistic: 103.2 on 1 and 947 DF, p-value: < 2.2e-164.1.1.3.2 Buffer 250m
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_high_2011b250, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.932 -5.623 -1.232 4.227 55.658
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.1746 0.3151 57.687 <2e-16 ***
## wSCOREMAT.2011 -84.8195 8.5197 -9.956 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.654 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.09475, Adjusted R-squared: 0.09379
## F-statistic: 99.12 on 1 and 947 DF, p-value: < 2.2e-164.1.1.3.3 Buffer 500m
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_high_2011b500, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -26.472 -5.196 -1.260 4.060 55.869
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.2182 0.3048 59.769 <2e-16 ***
## wSCOREMAT.2011 -80.1681 8.2427 -9.726 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.34 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.09082, Adjusted R-squared: 0.08986
## F-statistic: 94.59 on 1 and 947 DF, p-value: < 2.2e-164.1.1.3.4 Buffer 750m
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_high_2011b750, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.889 -5.029 -1.078 3.761 81.212
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.3174 0.3075 59.577 <2e-16 ***
## wSCOREMAT.2011 -77.0617 8.3142 -9.269 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.421 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.08317, Adjusted R-squared: 0.0822
## F-statistic: 85.91 on 1 and 947 DF, p-value: < 2.2e-164.1.2 BEI vs Pampalon | social
4.1.2.1 Bike lane length
Bike lane ratio to streets (in %)
4.1.2.1.1 Census tract level
ggplot(units::drop_units(bei_df), aes(y=Bike_lane.by.street.2011ct, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011ct ~ wSCORESOC.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011ct ~ wSCORESOC.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.711 -8.941 -2.409 4.872 91.128
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.8840 0.5597 15.873 <2e-16 ***
## wSCORESOC.2011 14.8411 14.5655 1.019 0.309
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.71 on 701 degrees of freedom
## (267 observations deleted due to missingness)
## Multiple R-squared: 0.001479, Adjusted R-squared: 5.44e-05
## F-statistic: 1.038 on 1 and 701 DF, p-value: 0.30864.1.2.1.2 Buffers 250m
ggplot(units::drop_units(bei_df), aes(y=Bike_lane.by.street.2011b250, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b250 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011b250 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.528 -5.806 -1.459 3.273 90.452
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.7613 0.4485 19.535 <2e-16 ***
## wSCORESOC.2011 23.5742 11.7408 2.008 0.045 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.583 on 720 degrees of freedom
## (248 observations deleted due to missingness)
## Multiple R-squared: 0.005568, Adjusted R-squared: 0.004187
## F-statistic: 4.032 on 1 and 720 DF, p-value: 0.045034.1.2.1.3 Buffers 500m
ggplot(units::drop_units(bei_df), aes(y=Bike_lane.by.street.2011b500, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b500 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011b500 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.830 -4.615 -0.960 2.985 56.915
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.4945 0.3255 26.095 < 2e-16 ***
## wSCORESOC.2011 25.1606 8.5360 2.948 0.00331 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.985 on 723 degrees of freedom
## (245 observations deleted due to missingness)
## Multiple R-squared: 0.01187, Adjusted R-squared: 0.01051
## F-statistic: 8.688 on 1 and 723 DF, p-value: 0.0033064.1.2.1.4 Buffers 750m
ggplot(units::drop_units(bei_df), aes(y=Bike_lane.by.street.2011b750, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b750 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011b750 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.558 -4.095 -0.827 2.508 56.198
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.5094 0.2802 30.370 < 2e-16 ***
## wSCORESOC.2011 20.1654 7.3710 2.736 0.00637 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.064 on 730 degrees of freedom
## (238 observations deleted due to missingness)
## Multiple R-squared: 0.01015, Adjusted R-squared: 0.008793
## F-statistic: 7.484 on 1 and 730 DF, p-value: 0.0063754.1.2.2 Canopy change
Measuring canopy (i.e. greenness ~ grass & trees) ratio within CT/buffer in 2011 (in %)
4.1.2.2.1 Census tract level
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_2011ct, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011ct ~ wSCORESOC.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011ct ~ wSCORESOC.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -55.985 -9.922 0.022 10.589 46.670
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 47.9152 0.6448 74.31 <2e-16 ***
## wSCORESOC.2011 -367.4519 18.2439 -20.14 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.96 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.2999, Adjusted R-squared: 0.2992
## F-statistic: 405.7 on 1 and 947 DF, p-value: < 2.2e-164.1.2.2.2 Buffer 250m
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_2011b250, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b250 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011b250 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.688 -8.473 -0.351 8.196 47.075
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.0933 0.5805 79.40 <2e-16 ***
## wSCORESOC.2011 -345.7804 16.4258 -21.05 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.27 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.3188, Adjusted R-squared: 0.3181
## F-statistic: 443.1 on 1 and 947 DF, p-value: < 2.2e-164.1.2.2.3 Buffer 500m
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_2011b500, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b500 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011b500 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -53.073 -7.791 -0.226 7.806 49.805
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.82 0.57 80.38 <2e-16 ***
## wSCORESOC.2011 -330.47 16.13 -20.49 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.99 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.3072, Adjusted R-squared: 0.3065
## F-statistic: 419.9 on 1 and 947 DF, p-value: < 2.2e-164.1.2.2.4 Buffer 750m
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_2011b750, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b750 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011b750 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -52.905 -8.180 -0.252 7.694 56.265
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 45.8440 0.5688 80.60 <2e-16 ***
## wSCORESOC.2011 -321.4983 16.0933 -19.98 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.96 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.2965, Adjusted R-squared: 0.2957
## F-statistic: 399.1 on 1 and 947 DF, p-value: < 2.2e-164.1.2.3 Canopy (trees) change
Measuring high canopy (i.e. trees only) ratio within CT/buffer in 2011 (in %)
4.1.2.3.1 Census tract level
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_high_2011ct, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011ct ~ wSCORESOC.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011ct ~ wSCORESOC.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.586 -7.331 -1.080 5.658 55.969
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20.4634 0.4304 47.541 < 2e-16 ***
## wSCORESOC.2011 -96.6304 12.1790 -7.934 5.96e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.32 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.06233, Adjusted R-squared: 0.06134
## F-statistic: 62.95 on 1 and 947 DF, p-value: 5.958e-154.1.2.3.2 Buffer 250m
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_high_2011b250, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b250 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011b250 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.374 -6.478 -0.976 4.994 52.468
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.4574 0.3725 52.235 < 2e-16 ***
## wSCORESOC.2011 -87.2741 10.5397 -8.281 4.15e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.798 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.06752, Adjusted R-squared: 0.06653
## F-statistic: 68.57 on 1 and 947 DF, p-value: 4.149e-164.1.2.3.3 Buffer 500m
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_high_2011b500, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b500 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011b500 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.181 -6.123 -0.941 4.868 52.894
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.4028 0.3606 53.807 < 2e-16 ***
## wSCORESOC.2011 -80.9793 10.2029 -7.937 5.84e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.485 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.06237, Adjusted R-squared: 0.06138
## F-statistic: 62.99 on 1 and 947 DF, p-value: 5.838e-154.1.2.3.4 Buffer 750m
ggplot(units::drop_units(bei_df), aes(y=pct_esp_vert_high_2011b750, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b750 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011b750 ~ wSCORESOC.2011, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.157 -5.940 -1.049 4.276 81.057
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.4523 0.3633 53.546 <2e-16 ***
## wSCORESOC.2011 -77.6318 10.2788 -7.553 1e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.556 on 947 degrees of freedom
## (21 observations deleted due to missingness)
## Multiple R-squared: 0.05681, Adjusted R-squared: 0.05582
## F-statistic: 57.04 on 1 and 947 DF, p-value: 1.003e-134.1.3 BEI vs gentrified CT
Gentrified CT in 2011
4.1.3.1 Bike lane length
Bike lane ratio to streets (in %)
4.1.3.1.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(y=Bike_lane.by.street.2011ct, x=gentrified_2011_2006)) +
geom_boxplot()## Warning: Removed 199 rows containing non-finite values (stat_boxplot).
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane.by.street.2011ct, na.rm = TRUE),
sd = sd(Bike_lane.by.street.2011ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane.by.street.2011ct ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 113 112.6 0.829 0.363
## Residuals 701 95266 135.9
## 267 observations deleted due to missingness4.1.3.1.2 Buffers 250m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(y=Bike_lane.by.street.2011b250, x=gentrified_2011_2006)) +
geom_boxplot()## Warning: Removed 183 rows containing non-finite values (stat_boxplot).
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane.by.street.2011b250, na.rm = TRUE),
sd = sd(Bike_lane.by.street.2011b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane.by.street.2011b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 3 2.89 0.032 0.859
## Residuals 720 66058 91.75
## 248 observations deleted due to missingness4.1.3.1.3 Buffers 500m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(y=Bike_lane.by.street.2011b500, x=gentrified_2011_2006)) +
geom_boxplot()## Warning: Removed 181 rows containing non-finite values (stat_boxplot).
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane.by.street.2011b500, na.rm = TRUE),
sd = sd(Bike_lane.by.street.2011b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane.by.street.2011b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 21 21.01 0.43 0.512
## Residuals 723 35349 48.89
## 245 observations deleted due to missingness4.1.3.1.4 Buffers 750m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(y=Bike_lane.by.street.2011b750, x=gentrified_2011_2006)) +
geom_boxplot()## Warning: Removed 174 rows containing non-finite values (stat_boxplot).
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane.by.street.2011b750, na.rm = TRUE),
sd = sd(Bike_lane.by.street.2011b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane.by.street.2011b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 36 35.90 0.974 0.324
## Residuals 730 26911 36.86
## 238 observations deleted due to missingness4.1.3.2 Canopy change
Measuring canopy (i.e. greenness ~ grass & trees) ratio within CT/buffer in 2011 (in %)
4.1.3.2.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(y=pct_esp_vert_2011ct, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_2011ct, na.rm = TRUE),
sd = sd(pct_esp_vert_2011ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_2011ct ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 44236 44236 121 <2e-16 ***
## Residuals 945 345538 366
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 23 observations deleted due to missingness4.1.3.2.2 Buffer 250m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(y=pct_esp_vert_2011b250, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_2011b250, na.rm = TRUE),
sd = sd(pct_esp_vert_2011b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_2011b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 34642 34642 113 <2e-16 ***
## Residuals 945 289603 306
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 23 observations deleted due to missingness4.1.3.2.3 Buffer 500m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(y=pct_esp_vert_2011b500, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_2011b500, na.rm = TRUE),
sd = sd(pct_esp_vert_2011b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_2011b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 31182 31182 106.9 <2e-16 ***
## Residuals 945 275548 292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 23 observations deleted due to missingness4.1.3.2.4 Buffer 750m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(y=pct_esp_vert_2011b750, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_2011b750, na.rm = TRUE),
sd = sd(pct_esp_vert_2011b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_2011b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 29134 29134 101.5 <2e-16 ***
## Residuals 945 271367 287
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 23 observations deleted due to missingness4.1.3.3 Canopy (trees) change
Measuring high canopy (i.e. trees only) ratio within CT/buffer in 2011 (in %)
4.1.3.3.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(y=pct_esp_vert_high_2011ct, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_high_2011ct, na.rm = TRUE),
sd = sd(pct_esp_vert_high_2011ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_high_2011ct ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 2815 2815 21.16 4.81e-06 ***
## Residuals 945 125732 133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 23 observations deleted due to missingness4.1.3.3.2 Buffer 250m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(y=pct_esp_vert_high_2011b250, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_high_2011b250, na.rm = TRUE),
sd = sd(pct_esp_vert_high_2011b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_high_2011b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 1780 1780.0 17.75 2.76e-05 ***
## Residuals 945 94772 100.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 23 observations deleted due to missingness4.1.3.3.3 Buffer 500m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(y=pct_esp_vert_high_2011b500, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_high_2011b500, na.rm = TRUE),
sd = sd(pct_esp_vert_high_2011b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_high_2011b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 1506 1506.3 16.12 6.42e-05 ***
## Residuals 945 88299 93.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 23 observations deleted due to missingness4.1.3.3.4 Buffer 750m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(y=pct_esp_vert_high_2011b750, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_high_2011b750, na.rm = TRUE),
sd = sd(pct_esp_vert_high_2011b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_high_2011b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 1368 1367.9 14.47 0.000151 ***
## Residuals 945 89326 94.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 23 observations deleted due to missingness4.2 INTERACT study area
# keep only interact CT
bei_df_aoi <- filter(bei_df, interact_aoi)4.2.1 BEI vs Pampalon | material
4.2.1.1 Bike lane length
Bike lane ratio to streets (in %)
4.2.1.1.1 Census tract level
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011ct, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.200 -7.709 -2.064 5.143 59.255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.8868 0.3611 24.613 < 2e-16 ***
## wSCOREMAT.2011 -27.1407 8.8021 -3.083 0.00213 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.409 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.01363, Adjusted R-squared: 0.0122
## F-statistic: 9.508 on 1 and 688 DF, p-value: 0.0021284.2.1.1.2 Buffers 250m
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011b250, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -12.4394 -5.3193 -0.8782 3.8947 24.9430
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.9081 0.2544 35.019 < 2e-16 ***
## wSCOREMAT.2011 -33.4497 6.2014 -5.394 9.49e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.629 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04057, Adjusted R-squared: 0.03918
## F-statistic: 29.09 on 1 and 688 DF, p-value: 9.492e-084.2.1.1.3 Buffers 500m
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011b500, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.7672 -3.9200 -0.7691 3.1333 20.7230
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.9691 0.2162 41.488 < 2e-16 ***
## wSCOREMAT.2011 -31.0529 5.2703 -5.892 5.97e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.634 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04804, Adjusted R-squared: 0.04665
## F-statistic: 34.72 on 1 and 688 DF, p-value: 5.971e-094.2.1.1.4 Buffers 750m
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011b750, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.5371 -3.5685 -0.5231 2.4075 18.3871
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.9986 0.1938 46.431 < 2e-16 ***
## wSCOREMAT.2011 -26.8673 4.7248 -5.686 1.92e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.05 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.04489, Adjusted R-squared: 0.0435
## F-statistic: 32.34 on 1 and 688 DF, p-value: 1.918e-084.2.1.2 Canopy change
Measuring canopy (i.e. greenness ~ grass & trees) ratio within CT/buffer in 2011 (in %)
4.2.1.2.1 Census tract level
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011ct, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -48.859 -11.624 -0.331 8.977 47.657
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.9617 0.5933 63.988 < 2e-16 ***
## wSCOREMAT.2011 -109.4130 14.4631 -7.565 1.25e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.46 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.07679, Adjusted R-squared: 0.07545
## F-statistic: 57.23 on 1 and 688 DF, p-value: 1.247e-134.2.1.2.2 Buffer 250m
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011b250, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -41.973 -8.235 -0.954 7.170 41.284
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.0871 0.4897 73.690 < 2e-16 ***
## wSCOREMAT.2011 -90.8096 11.9387 -7.606 9.29e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.76 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.07757, Adjusted R-squared: 0.07623
## F-statistic: 57.86 on 1 and 688 DF, p-value: 9.289e-144.2.1.2.3 Buffer 500m
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011b500, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.955 -8.076 -0.976 7.123 40.579
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.1464 0.4645 77.820 < 2e-16 ***
## wSCOREMAT.2011 -83.2220 11.3236 -7.349 5.66e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 12.1 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.07279, Adjusted R-squared: 0.07145
## F-statistic: 54.01 on 1 and 688 DF, p-value: 5.662e-134.2.1.2.4 Buffer 750m
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011b750, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -38.621 -8.282 -0.894 7.305 39.867
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 36.211 0.451 80.287 < 2e-16 ***
## wSCOREMAT.2011 -76.126 10.995 -6.923 1.01e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11.75 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.06513, Adjusted R-squared: 0.06377
## F-statistic: 47.93 on 1 and 688 DF, p-value: 1.013e-114.2.1.3 Canopy (trees) change
Measuring high canopy (i.e. trees only) ratio within CT/buffer in 2011 (in %)
4.2.1.3.1 Census tract level
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011ct, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011ct ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.954 -5.492 -0.802 4.630 44.704
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.8069 0.3379 55.66 <2e-16 ***
## wSCOREMAT.2011 -96.7463 8.2370 -11.74 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.805 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.167, Adjusted R-squared: 0.1658
## F-statistic: 138 on 1 and 688 DF, p-value: < 2.2e-164.2.1.3.2 Buffer 250m
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011b250, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011b250 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.9166 -4.2910 -0.8451 3.4739 27.8788
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.6539 0.2668 66.17 <2e-16 ***
## wSCOREMAT.2011 -81.7658 6.5045 -12.57 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.953 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1868, Adjusted R-squared: 0.1856
## F-statistic: 158 on 1 and 688 DF, p-value: < 2.2e-164.2.1.3.3 Buffer 500m
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011b500, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011b500 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.4603 -4.0485 -0.9957 3.5770 26.5204
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.738 0.250 70.96 <2e-16 ***
## wSCOREMAT.2011 -76.880 6.094 -12.62 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.515 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1879, Adjusted R-squared: 0.1867
## F-statistic: 159.1 on 1 and 688 DF, p-value: < 2.2e-164.2.1.3.4 Buffer 750m
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011b750, x=wSCOREMAT.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011b750 ~ wSCOREMAT.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -24.6658 -3.9100 -0.6752 3.4947 25.5210
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 17.7572 0.2377 74.69 <2e-16 ***
## wSCOREMAT.2011 -72.9660 5.7960 -12.59 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.196 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.1872, Adjusted R-squared: 0.186
## F-statistic: 158.5 on 1 and 688 DF, p-value: < 2.2e-164.2.2 BEI vs Pampalon | social
4.2.2.1 Bike lane length
Bike lane ratio to streets (in %)
4.2.2.1.1 Census tract level
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011ct, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011ct ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011ct ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.126 -8.483 -1.894 5.239 55.220
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.4115 0.4605 18.267 <2e-16 ***
## wSCORESOC.2011 13.9299 11.9135 1.169 0.243
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.464 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.001983, Adjusted R-squared: 0.0005326
## F-statistic: 1.367 on 1 and 688 DF, p-value: 0.24274.2.2.1.2 Buffers 250m
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011b250, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b250 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011b250 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.6670 -4.9958 -0.7933 3.8783 24.9297
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.2950 0.3282 25.277 <2e-16 ***
## wSCORESOC.2011 18.3043 8.4904 2.156 0.0314 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.745 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.00671, Adjusted R-squared: 0.005267
## F-statistic: 4.648 on 1 and 688 DF, p-value: 0.031444.2.2.1.3 Buffers 500m
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011b500, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b500 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011b500 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.4083 -4.1527 -0.6607 3.1263 20.5500
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.3114 0.2793 29.761 < 2e-16 ***
## wSCORESOC.2011 20.6704 7.2254 2.861 0.00435 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.74 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.01176, Adjusted R-squared: 0.01032
## F-statistic: 8.184 on 1 and 688 DF, p-value: 0.0043544.2.2.1.4 Buffers 750m
ggplot(units::drop_units(bei_df_aoi), aes(y=Bike_lane.by.street.2011b750, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(Bike_lane.by.street.2011b750 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = Bike_lane.by.street.2011b750 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.4108 -3.7743 -0.6908 2.4571 19.2676
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.3853 0.2497 33.575 < 2e-16 ***
## wSCORESOC.2011 19.7214 6.4615 3.052 0.00236 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.133 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.01336, Adjusted R-squared: 0.01192
## F-statistic: 9.315 on 1 and 688 DF, p-value: 0.002364.2.2.2 Canopy change
Measuring canopy (i.e. greenness ~ grass & trees) ratio within CT/buffer in 2011 (in %)
4.2.2.2.1 Census tract level
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011ct, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011ct ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011ct ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -41.160 -8.631 -0.185 7.616 48.600
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 44.829 0.637 70.37 <2e-16 ***
## wSCORESOC.2011 -308.789 16.482 -18.73 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13.09 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.3378, Adjusted R-squared: 0.3369
## F-statistic: 351 on 1 and 688 DF, p-value: < 2.2e-164.2.2.2.2 Buffer 250m
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011b250, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b250 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011b250 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.464 -6.484 -0.223 5.905 40.372
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 42.0944 0.5106 82.44 <2e-16 ***
## wSCORESOC.2011 -269.0628 13.2111 -20.37 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.5 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.3761, Adjusted R-squared: 0.3752
## F-statistic: 414.8 on 1 and 688 DF, p-value: < 2.2e-164.2.2.2.3 Buffer 500m
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011b500, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b500 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011b500 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.562 -6.357 -0.285 6.124 37.934
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.7528 0.4873 85.68 <2e-16 ***
## wSCORESOC.2011 -250.7765 12.6084 -19.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.02 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.3651, Adjusted R-squared: 0.3642
## F-statistic: 395.6 on 1 and 688 DF, p-value: < 2.2e-164.2.2.2.4 Buffer 750m
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_2011b750, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_2011b750 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_2011b750 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -25.132 -6.646 -0.006 5.852 36.685
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 41.573 0.475 87.52 <2e-16 ***
## wSCORESOC.2011 -239.092 12.290 -19.45 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.764 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.3549, Adjusted R-squared: 0.3539
## F-statistic: 378.4 on 1 and 688 DF, p-value: < 2.2e-164.2.2.3 Canopy (trees) change
Measuring high canopy (i.e. trees only) ratio within CT/buffer in 2011 (in %)
4.2.2.3.1 Census tract level
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011ct, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011ct ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011ct ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.424 -6.539 -1.281 4.979 45.812
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 19.9448 0.4585 43.496 < 2e-16 ***
## wSCORESOC.2011 -68.0181 11.8637 -5.733 1.47e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.425 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0456, Adjusted R-squared: 0.04421
## F-statistic: 32.87 on 1 and 688 DF, p-value: 1.475e-084.2.2.3.2 Buffer 250m
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011b250, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b250 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011b250 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -15.390 -5.380 -1.162 3.970 31.006
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.4134 0.3681 50.025 < 2e-16 ***
## wSCORESOC.2011 -49.0855 9.5232 -5.154 3.33e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.565 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.03718, Adjusted R-squared: 0.03578
## F-statistic: 26.57 on 1 and 688 DF, p-value: 3.331e-074.2.2.3.3 Buffer 500m
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011b500, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b500 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011b500 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.032 -5.059 -1.291 4.093 29.050
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.3659 0.3461 53.070 < 2e-16 ***
## wSCORESOC.2011 -42.5593 8.9537 -4.753 2.44e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.113 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.0318, Adjusted R-squared: 0.03039
## F-statistic: 22.59 on 1 and 688 DF, p-value: 2.438e-064.2.2.3.4 Buffer 750m
ggplot(units::drop_units(bei_df_aoi), aes(y=pct_esp_vert_high_2011b750, x=wSCORESOC.2011)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(pct_esp_vert_high_2011b750 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = pct_esp_vert_high_2011b750 ~ wSCORESOC.2011, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -13.114 -5.005 -1.117 3.803 26.489
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.3146 0.3294 55.595 < 2e-16 ***
## wSCORESOC.2011 -38.8036 8.5231 -4.553 6.26e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6.771 on 688 degrees of freedom
## (15 observations deleted due to missingness)
## Multiple R-squared: 0.02925, Adjusted R-squared: 0.02784
## F-statistic: 20.73 on 1 and 688 DF, p-value: 6.262e-064.2.3 BEI vs gentrified CT
Gentrified CT in 2011
4.2.3.1 Bike lane length
Bike lane ratio to streets (in %)
4.2.3.1.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(y=Bike_lane.by.street.2011ct, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane.by.street.2011ct, na.rm = TRUE),
sd = sd(Bike_lane.by.street.2011ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane.by.street.2011ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 5 4.95 0.056 0.813
## Residuals 688 60902 88.52
## 15 observations deleted due to missingness4.2.3.1.2 Buffers 250m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(y=Bike_lane.by.street.2011b250, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane.by.street.2011b250, na.rm = TRUE),
sd = sd(Bike_lane.by.street.2011b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane.by.street.2011b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 2 1.67 0.037 0.847
## Residuals 688 31057 45.14
## 15 observations deleted due to missingness4.2.3.1.3 Buffers 500m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(y=Bike_lane.by.street.2011b500, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane.by.street.2011b500, na.rm = TRUE),
sd = sd(Bike_lane.by.street.2011b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane.by.street.2011b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 22 21.78 0.664 0.416
## Residuals 688 22580 32.82
## 15 observations deleted due to missingness4.2.3.1.4 Buffers 750m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(y=Bike_lane.by.street.2011b750, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane.by.street.2011b750, na.rm = TRUE),
sd = sd(Bike_lane.by.street.2011b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane.by.street.2011b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 41 40.84 1.547 0.214
## Residuals 688 18163 26.40
## 15 observations deleted due to missingness4.2.3.2 Canopy change
Measuring canopy (i.e. greenness ~ grass & trees) ratio within CT/buffer in 2011 (in %)
4.2.3.2.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(y=pct_esp_vert_2011ct, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_2011ct, na.rm = TRUE),
sd = sd(pct_esp_vert_2011ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_2011ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 23284 23284 102.9 <2e-16 ***
## Residuals 688 155714 226
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness4.2.3.2.2 Buffer 250m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(y=pct_esp_vert_2011b250, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_2011b250, na.rm = TRUE),
sd = sd(pct_esp_vert_2011b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_2011b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 14776 14776 94.56 <2e-16 ***
## Residuals 688 107511 156
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness4.2.3.2.3 Buffer 500m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(y=pct_esp_vert_2011b500, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_2011b500, na.rm = TRUE),
sd = sd(pct_esp_vert_2011b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_2011b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 12682 12682 90.12 <2e-16 ***
## Residuals 688 96815 141
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness4.2.3.2.4 Buffer 750m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(y=pct_esp_vert_2011b750, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_2011b750, na.rm = TRUE),
sd = sd(pct_esp_vert_2011b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_2011b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 11029 11029 82.95 <2e-16 ***
## Residuals 688 91471 133
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness4.2.3.3 Canopy (trees) change
Measuring high canopy (i.e. trees only) ratio within CT/buffer in 2011 (in %)
4.2.3.3.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(y=pct_esp_vert_high_2011ct, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_high_2011ct, na.rm = TRUE),
sd = sd(pct_esp_vert_high_2011ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_high_2011ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 1856 1855.8 20.54 6.89e-06 ***
## Residuals 688 62160 90.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness4.2.3.3.2 Buffer 250m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(y=pct_esp_vert_high_2011b250, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_high_2011b250, na.rm = TRUE),
sd = sd(pct_esp_vert_high_2011b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_high_2011b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 786 786.4 13.43 0.000266 ***
## Residuals 688 40275 58.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness4.2.3.3.3 Buffer 500m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(y=pct_esp_vert_high_2011b500, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_high_2011b500, na.rm = TRUE),
sd = sd(pct_esp_vert_high_2011b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_high_2011b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 632 632.0 12.27 0.000491 ***
## Residuals 688 35449 51.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness4.2.3.3.4 Buffer 750m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(y=pct_esp_vert_high_2011b750, x=gentrified_2011_2006)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_high_2011b750, na.rm = TRUE),
sd = sd(pct_esp_vert_high_2011b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_high_2011b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 476 476.4 10.2 0.00147 **
## Residuals 688 32147 46.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness5 LEGACY STUFF | FOR REFERENCE ONLY
5.1 Association between SES and BEI variables
5.1.1 Pampalon | material vs. BEI
5.1.1.1 Bike lane length
Measuring bike lane length change within CT/buffers (in meters) between 2011 and 2016
5.1.1.1.1 Census tract level
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.2011.2016ct, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016ct, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016ct, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14093 -0.02165 -0.00058 0.02353 0.13849
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.904e-04 1.219e-03 0.32 0.7488
## Bike_lane_diff.2011.2016ct -3.201e-06 1.301e-06 -2.46 0.0141 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03602 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.006345, Adjusted R-squared: 0.005297
## F-statistic: 6.053 on 1 and 948 DF, p-value: 0.014065.1.1.1.2 Buffers 250m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.2011.2016b250, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016b250, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016b250, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.141558 -0.021775 -0.000925 0.023297 0.137860
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.016e-03 1.277e-03 0.796 0.42622
## Bike_lane_diff.2011.2016b250 -2.258e-06 7.906e-07 -2.856 0.00438 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03598 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.008533, Adjusted R-squared: 0.007487
## F-statistic: 8.159 on 1 and 948 DF, p-value: 0.0043785.1.1.1.3 Buffers 500m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.2011.2016b500, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016b500, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016b500, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.142173 -0.021775 -0.000562 0.023345 0.137246
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.630e-03 1.328e-03 1.228 0.21993
## Bike_lane_diff.2011.2016b500 -1.772e-06 5.396e-07 -3.284 0.00106 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03593 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.01125, Adjusted R-squared: 0.01021
## F-statistic: 10.79 on 1 and 948 DF, p-value: 0.001065.1.1.1.4 Buffers 750m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.2011.2016b750, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016b750, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016b750, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.142411 -0.022130 -0.000503 0.022855 0.137007
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.869e-03 1.372e-03 1.362 0.17341
## Bike_lane_diff.2011.2016b750 -1.258e-06 3.904e-07 -3.221 0.00132 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03594 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.01083, Adjusted R-squared: 0.009782
## F-statistic: 10.38 on 1 and 948 DF, p-value: 0.0013215.1.1.2 Bike lane / street
Measuring bike lane length change, normalized by length of streets within CT/buffers (in meters)
5.1.1.2.1 Census tract level
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.street.2011.2016ct, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016ct, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016ct,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.140946 -0.025988 0.001677 0.026813 0.138472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0004043 0.0016064 0.252 0.801
## Bike_lane_diff.by.street.2011.2016ct -0.0001646 0.0001551 -1.061 0.289
##
## Residual standard error: 0.03978 on 702 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.001602, Adjusted R-squared: 0.0001796
## F-statistic: 1.126 on 1 and 702 DF, p-value: 0.28895.1.1.2.2 Buffer 250m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.street.2011.2016b250, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016b250, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016b250,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.142037 -0.025277 0.001961 0.026047 0.137382
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0014946 0.0016761 0.892 0.373
## Bike_lane_diff.by.street.2011.2016b250 -0.0005511 0.0002453 -2.247 0.025 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03926 on 721 degrees of freedom
## (247 observations deleted due to missingness)
## Multiple R-squared: 0.006952, Adjusted R-squared: 0.005574
## F-statistic: 5.047 on 1 and 721 DF, p-value: 0.024975.1.1.2.3 Buffer 500m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.street.2011.2016b500, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016b500, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016b500,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.142775 -0.025498 0.001986 0.026003 0.136643
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0022332 0.0017686 1.263 0.20711
## Bike_lane_diff.by.street.2011.2016b500 -0.0008014 0.0003025 -2.649 0.00825
##
## (Intercept)
## Bike_lane_diff.by.street.2011.2016b500 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03916 on 724 degrees of freedom
## (244 observations deleted due to missingness)
## Multiple R-squared: 0.009598, Adjusted R-squared: 0.00823
## F-statistic: 7.016 on 1 and 724 DF, p-value: 0.0082535.1.1.2.4 Buffer 750m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.street.2011.2016b750, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016b750, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016b750,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.142956 -0.025333 0.002213 0.026062 0.136463
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0024140 0.0018380 1.313 0.18946
## Bike_lane_diff.by.street.2011.2016b750 -0.0009010 0.0003432 -2.625 0.00884
##
## (Intercept)
## Bike_lane_diff.by.street.2011.2016b750 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03906 on 731 degrees of freedom
## (237 observations deleted due to missingness)
## Multiple R-squared: 0.00934, Adjusted R-squared: 0.007985
## F-statistic: 6.892 on 1 and 731 DF, p-value: 0.0088385.1.1.3 Bike lane / area
Measuring bike lane length change, normalized by area within CT/buffers (in meters)
5.1.1.3.1 Census tract level
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.area.2011.2016ct, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016ct, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016ct,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.140461 -0.022162 -0.000097 0.023480 0.138958
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.102e-05 1.265e-03 -0.064 0.949
## Bike_lane_diff.by.area.2011.2016ct -8.785e-04 1.104e-03 -0.796 0.426
##
## Residual standard error: 0.03612 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.0006678, Adjusted R-squared: -0.0003864
## F-statistic: 0.6335 on 1 and 948 DF, p-value: 0.42635.1.1.3.2 Buffer 250m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.area.2011.2016b250, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016b250, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016b250,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.141396 -0.022428 -0.000406 0.023373 0.138023
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0008536 0.0013002 0.657 0.5117
## Bike_lane_diff.by.area.2011.2016b250 -0.0032359 0.0014014 -2.309 0.0212 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03603 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.005593, Adjusted R-squared: 0.004544
## F-statistic: 5.332 on 1 and 948 DF, p-value: 0.021155.1.1.3.3 Buffer 500m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.area.2011.2016b500, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016b500, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016b500,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.141827 -0.021977 -0.000228 0.023279 0.137592
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001285 0.001332 0.965 0.33497
## Bike_lane_diff.by.area.2011.2016b500 -0.004451 0.001634 -2.724 0.00657 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03599 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.007765, Adjusted R-squared: 0.006718
## F-statistic: 7.419 on 1 and 948 DF, p-value: 0.0065735.1.1.3.4 Buffer 750m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.area.2011.2016b750, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016b750, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016b750,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.141943 -0.022225 -0.000778 0.023093 0.137475
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001401 0.001357 1.033 0.30193
## Bike_lane_diff.by.area.2011.2016b750 -0.004838 0.001794 -2.697 0.00713 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03599 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.007613, Adjusted R-squared: 0.006566
## F-statistic: 7.272 on 1 and 948 DF, p-value: 0.0071265.1.1.4 Canopy change
Measuring canopy (i.e. greenness) ratio change within CT/buffer between 2011 and 2017 (in %)
5.1.1.4.1 Census tract level
ggplot(units::drop_units(bei_df), aes(x=pct_esp_vert_diff_2011.2017ct, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017ct, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017ct,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.141177 -0.022286 0.000108 0.024187 0.144043
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0018129 0.0015560 1.165 0.244
## pct_esp_vert_diff_2011.2017ct -0.0004790 0.0002163 -2.215 0.027 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03604 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.005147, Adjusted R-squared: 0.004097
## F-statistic: 4.905 on 1 and 948 DF, p-value: 0.027025.1.1.4.2 Buffer 250m
ggplot(units::drop_units(bei_df), aes(x=pct_esp_vert_diff_2011.2017b250, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017b250, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017b250,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.140637 -0.022129 0.000271 0.024096 0.139629
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0028608 0.0015659 1.827 0.06803 .
## pct_esp_vert_diff_2011.2017b250 -0.0007240 0.0002278 -3.178 0.00153 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03594 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.01054, Adjusted R-squared: 0.009497
## F-statistic: 10.1 on 1 and 948 DF, p-value: 0.0015325.1.1.4.3 Buffer 500m
ggplot(units::drop_units(bei_df), aes(x=pct_esp_vert_diff_2011.2017b500, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017b500, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017b500,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.140177 -0.022065 0.000376 0.023922 0.139495
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0029249 0.0015828 1.848 0.06493 .
## pct_esp_vert_diff_2011.2017b500 -0.0007428 0.0002348 -3.163 0.00161 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03594 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.01044, Adjusted R-squared: 0.009401
## F-statistic: 10.01 on 1 and 948 DF, p-value: 0.001615.1.1.4.4 Buffer 750m
ggplot(units::drop_units(bei_df), aes(x=pct_esp_vert_diff_2011.2017b750, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017b750, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017b750,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.140199 -0.021925 0.000296 0.023997 0.139261
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0033072 0.0016028 2.063 0.039349 *
## pct_esp_vert_diff_2011.2017b750 -0.0008314 0.0002429 -3.423 0.000646 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03591 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.01221, Adjusted R-squared: 0.01117
## F-statistic: 11.72 on 1 and 948 DF, p-value: 0.00064575.1.2 Pampalon | social vs. BEI
5.1.2.1 Bike lane length
Measuring bike lane length change within CT/buffers (in meters) between 2011 and 2016
5.1.2.1.1 Census tract level
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.2011.2016ct, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.2011.2016ct, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.2011.2016ct, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.076895 -0.022870 0.000003 0.024011 0.094864
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.810e-02 9.833e-04 18.411 <2e-16 ***
## Bike_lane_diff.2011.2016ct -2.671e-07 1.050e-06 -0.254 0.799
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02906 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 6.83e-05, Adjusted R-squared: -0.0009865
## F-statistic: 0.06475 on 1 and 948 DF, p-value: 0.79925.1.2.1.2 Buffers 250m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.2011.2016b250, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.2011.2016b250, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.2011.2016b250, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.075420 -0.022369 0.000121 0.023588 0.096340
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.663e-02 1.025e-03 16.221 < 2e-16 ***
## Bike_lane_diff.2011.2016b250 2.148e-06 6.348e-07 3.383 0.000746 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02889 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.01193, Adjusted R-squared: 0.01089
## F-statistic: 11.45 on 1 and 948 DF, p-value: 0.00074595.1.2.1.3 Buffers 500m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.2011.2016b500, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.2011.2016b500, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.2011.2016b500, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.087146 -0.021312 0.000382 0.021537 0.098092
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.488e-02 1.053e-03 14.130 < 2e-16 ***
## Bike_lane_diff.2011.2016b500 2.675e-06 4.278e-07 6.253 6.09e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02848 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.03961, Adjusted R-squared: 0.0386
## F-statistic: 39.1 on 1 and 948 DF, p-value: 6.089e-105.1.2.1.4 Buffers 750m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.2011.2016b750, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.2011.2016b750, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.2011.2016b750, data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.089263 -0.020055 0.000102 0.020760 0.099702
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.327e-02 1.070e-03 12.398 <2e-16 ***
## Bike_lane_diff.2011.2016b750 2.573e-06 3.045e-07 8.449 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02803 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.07003, Adjusted R-squared: 0.06905
## F-statistic: 71.38 on 1 and 948 DF, p-value: < 2.2e-165.1.2.2 Bike lane / street
Measuring bike lane length change, normalized by length of streets within CT/buffers (in meters)
5.1.2.2.1 Census tract level
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.street.2011.2016ct, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016ct, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016ct,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.099465 -0.020801 0.003917 0.021838 0.091426
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0215436 0.0011694 18.423 < 2e-16 ***
## Bike_lane_diff.by.street.2011.2016ct 0.0003846 0.0001129 3.407 0.000695 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02896 on 702 degrees of freedom
## (266 observations deleted due to missingness)
## Multiple R-squared: 0.01626, Adjusted R-squared: 0.01486
## F-statistic: 11.6 on 1 and 702 DF, p-value: 0.00069545.1.2.2.2 Buffer 250m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.street.2011.2016b250, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016b250, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016b250,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.085760 -0.020329 0.003543 0.020790 0.094580
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0183891 0.0012090 15.210 < 2e-16
## Bike_lane_diff.by.street.2011.2016b250 0.0012432 0.0001769 7.027 4.91e-12
##
## (Intercept) ***
## Bike_lane_diff.by.street.2011.2016b250 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02832 on 721 degrees of freedom
## (247 observations deleted due to missingness)
## Multiple R-squared: 0.06409, Adjusted R-squared: 0.06279
## F-statistic: 49.37 on 1 and 721 DF, p-value: 4.906e-125.1.2.2.3 Buffer 500m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.street.2011.2016b500, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016b500, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016b500,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.077409 -0.020433 0.002393 0.020354 0.096520
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.016449 0.001262 13.030 < 2e-16 ***
## Bike_lane_diff.by.street.2011.2016b500 0.001804 0.000216 8.354 3.36e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02795 on 724 degrees of freedom
## (244 observations deleted due to missingness)
## Multiple R-squared: 0.08791, Adjusted R-squared: 0.08665
## F-statistic: 69.78 on 1 and 724 DF, p-value: 3.364e-165.1.2.2.4 Buffer 750m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.street.2011.2016b750, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016b750, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016b750,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.081760 -0.019639 0.002058 0.019899 0.098165
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0148036 0.0013035 11.357 <2e-16
## Bike_lane_diff.by.street.2011.2016b750 0.0022461 0.0002434 9.228 <2e-16
##
## (Intercept) ***
## Bike_lane_diff.by.street.2011.2016b750 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0277 on 731 degrees of freedom
## (237 observations deleted due to missingness)
## Multiple R-squared: 0.1043, Adjusted R-squared: 0.1031
## F-statistic: 85.16 on 1 and 731 DF, p-value: < 2.2e-165.1.2.3 Bike lane / area
Measuring bike lane length change, normalized by area within CT/buffers (in meters)
5.1.2.3.1 Census tract level
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.area.2011.2016ct, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016ct, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016ct,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073460 -0.021104 0.000026 0.019814 0.098300
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0146689 0.0009759 15.031 <2e-16 ***
## Bike_lane_diff.by.area.2011.2016ct 0.0077844 0.0008513 9.144 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02786 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.08105, Adjusted R-squared: 0.08008
## F-statistic: 83.61 on 1 and 948 DF, p-value: < 2.2e-165.1.2.3.2 Buffer 250m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.area.2011.2016b250, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016b250, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016b250,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.071772 -0.019890 -0.000031 0.019665 0.099988
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0129809 0.0009795 13.25 <2e-16 ***
## Bike_lane_diff.by.area.2011.2016b250 0.0124400 0.0010557 11.78 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02714 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.1278, Adjusted R-squared: 0.1268
## F-statistic: 138.8 on 1 and 948 DF, p-value: < 2.2e-165.1.2.3.3 Buffer 500m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.area.2011.2016b500, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016b500, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016b500,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.072685 -0.019455 -0.000541 0.019379 0.101196
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0117733 0.0009891 11.90 <2e-16 ***
## Bike_lane_diff.by.area.2011.2016b500 0.0159618 0.0012134 13.15 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02673 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.1544, Adjusted R-squared: 0.1535
## F-statistic: 173 on 1 and 948 DF, p-value: < 2.2e-165.1.2.3.4 Buffer 750m
ggplot(units::drop_units(bei_df), aes(x=Bike_lane_diff.by.area.2011.2016b750, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016b750, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016b750,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.072425 -0.018691 -0.000239 0.018510 0.102041
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0109278 0.0009971 10.96 <2e-16 ***
## Bike_lane_diff.by.area.2011.2016b750 0.0184641 0.0013187 14.00 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02646 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.1714, Adjusted R-squared: 0.1705
## F-statistic: 196.1 on 1 and 948 DF, p-value: < 2.2e-165.1.2.4 Canopy change
Measuring canopy (i.e. greenness) ratio change within CT/buffer between 2011 and 2017 (in %)
5.1.2.4.1 Census tract level
ggplot(units::drop_units(bei_df), aes(x=pct_esp_vert_diff_2011.2017ct, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017ct, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017ct,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.077181 -0.022926 -0.000038 0.023861 0.094607
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0187165 0.0012544 14.921 <2e-16 ***
## pct_esp_vert_diff_2011.2017ct -0.0001439 0.0001744 -0.825 0.41
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02905 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.0007176, Adjusted R-squared: -0.0003365
## F-statistic: 0.6808 on 1 and 948 DF, p-value: 0.40955.1.2.4.2 Buffer 250m
ggplot(units::drop_units(bei_df), aes(x=pct_esp_vert_diff_2011.2017b250, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017b250, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017b250,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.074810 -0.022881 0.000216 0.024048 0.094711
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0193741 0.0012646 15.320 <2e-16 ***
## pct_esp_vert_diff_2011.2017b250 -0.0002922 0.0001840 -1.588 0.113
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02902 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.002653, Adjusted R-squared: 0.001601
## F-statistic: 2.522 on 1 and 948 DF, p-value: 0.11265.1.2.4.3 Buffer 500m
ggplot(units::drop_units(bei_df), aes(x=pct_esp_vert_diff_2011.2017b500, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017b500, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017b500,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.074909 -0.022909 0.000083 0.023970 0.094889
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0196867 0.0012774 15.411 <2e-16 ***
## pct_esp_vert_diff_2011.2017b500 -0.0003627 0.0001895 -1.914 0.056 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02901 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.003849, Adjusted R-squared: 0.002798
## F-statistic: 3.663 on 1 and 948 DF, p-value: 0.055955.1.2.4.4 Buffer 750m
ggplot(units::drop_units(bei_df), aes(x=pct_esp_vert_diff_2011.2017b750, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017b750, data = units::drop_units(bei_df))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017b750,
## data = units::drop_units(bei_df))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07479 -0.02299 -0.00016 0.02398 0.09487
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0200264 0.0012938 15.479 <2e-16 ***
## pct_esp_vert_diff_2011.2017b750 -0.0004397 0.0001961 -2.243 0.0251 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02899 on 948 degrees of freedom
## (20 observations deleted due to missingness)
## Multiple R-squared: 0.005279, Adjusted R-squared: 0.004229
## F-statistic: 5.031 on 1 and 948 DF, p-value: 0.025135.1.3 Gentrified CT | 2016
5.1.3.1 Bike lane length
Measuring bike lane length change within CT/buffers (in meters) between 2011 and 2016
5.1.3.1.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=Bike_lane_diff.2011.2016ct, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016ct, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016ct ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 205247 205247 0.253 0.615
## Residuals 945 765953237 810533
## 23 observations deleted due to missingness5.1.3.1.2 Buffers 250m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=Bike_lane_diff.2011.2016b250, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b250, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 2.977e+07 29772990 13.79 0.000216 ***
## Residuals 945 2.040e+09 2159034
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 23 observations deleted due to missingness5.1.3.1.3 Buffers 500m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=Bike_lane_diff.2011.2016b500, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b500, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 1.594e+08 159408890 35.28 4.01e-09 ***
## Residuals 945 4.270e+09 4518550
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 23 observations deleted due to missingness5.1.3.1.4 Buffers 750m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=Bike_lane_diff.2011.2016b750, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b750, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 4.517e+08 451658967 53.32 6.02e-13 ***
## Residuals 945 8.005e+09 8471075
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 23 observations deleted due to missingness5.1.3.2 Bike lane / street
Measuring bike lane length change, normalized by length of streets within CT/buffers (in meters)
5.1.3.2.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=Bike_lane_diff.by.street.2011.2016ct, y=gentrified_2016_2011)) +
geom_boxplot()## Warning: Removed 244 rows containing non-finite values (stat_boxplot).
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.street.2011.2016ct, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.street.2011.2016ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.street.2011.2016ct ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 784 784.4 8.461 0.00374 **
## Residuals 701 64986 92.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 267 observations deleted due to missingness5.1.3.2.2 Buffers 250m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=Bike_lane_diff.by.street.2011.2016b250, y=gentrified_2016_2011)) +
geom_boxplot()## Warning: Removed 225 rows containing non-finite values (stat_boxplot).
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.street.2011.2016b250, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.street.2011.2016b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.street.2011.2016b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 1172 1172 34.53 6.43e-09 ***
## Residuals 720 24449 34
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 248 observations deleted due to missingness5.1.3.2.3 Buffers 500m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=Bike_lane_diff.by.street.2011.2016b500, y=gentrified_2016_2011)) +
geom_boxplot()## Warning: Removed 222 rows containing non-finite values (stat_boxplot).
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.street.2011.2016b500, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.street.2011.2016b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.street.2011.2016b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 1045 1045.2 48.14 8.83e-12 ***
## Residuals 723 15697 21.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 245 observations deleted due to missingness5.1.3.2.4 Buffers 750m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=Bike_lane_diff.by.street.2011.2016b750, y=gentrified_2016_2011)) +
geom_boxplot()## Warning: Removed 215 rows containing non-finite values (stat_boxplot).
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.street.2011.2016b750, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.street.2011.2016b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.street.2011.2016b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 844 844.0 50.98 2.26e-12 ***
## Residuals 730 12086 16.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 238 observations deleted due to missingness5.1.3.3 Bike lane / area
Measuring bike lane length change, normalized by area within CT/buffers (in meters)
5.1.3.3.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=Bike_lane_diff.by.area.2011.2016ct, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.area.2011.2016ct, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.area.2011.2016ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.area.2011.2016ct ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 61.1 61.09 57.2 9.34e-14 ***
## Residuals 945 1009.3 1.07
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 23 observations deleted due to missingness5.1.3.3.2 Buffers 250m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=Bike_lane_diff.by.area.2011.2016b250, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.area.2011.2016b250, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.area.2011.2016b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.area.2011.2016b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 66.9 66.86 106.4 <2e-16 ***
## Residuals 945 593.7 0.63
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 23 observations deleted due to missingness5.1.3.3.3 Buffers 500m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=Bike_lane_diff.by.area.2011.2016b500, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.area.2011.2016b500, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.area.2011.2016b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.area.2011.2016b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 58.6 58.57 130.1 <2e-16 ***
## Residuals 945 425.3 0.45
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 23 observations deleted due to missingness5.1.3.3.4 Buffers 750m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=Bike_lane_diff.by.area.2011.2016b750, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.area.2011.2016b750, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.area.2011.2016b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.area.2011.2016b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 49.0 49.05 131.7 <2e-16 ***
## Residuals 945 351.9 0.37
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 23 observations deleted due to missingness5.1.3.4 Canopy change
5.1.3.4.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=pct_esp_vert_diff_2011.2017ct, y=gentrified_2016_2011)) +
geom_boxplot() 
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017ct, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017ct ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 0 0.033 0.001 0.973
## Residuals 945 27144 28.724
## 23 observations deleted due to missingness5.1.3.4.2 Buffer 250m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=pct_esp_vert_diff_2011.2017b250, y=gentrified_2016_2011)) +
geom_boxplot() 
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b250, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 6 5.94 0.232 0.63
## Residuals 945 24218 25.63
## 23 observations deleted due to missingness5.1.3.4.3 Buffer 500m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=pct_esp_vert_diff_2011.2017b500, y=gentrified_2016_2011)) +
geom_boxplot() 
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b500, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 6 5.686 0.236 0.627
## Residuals 945 22742 24.066
## 23 observations deleted due to missingness5.1.3.4.4 Buffer 750m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2016_2011), aes(x=pct_esp_vert_diff_2011.2017b750, y=gentrified_2016_2011)) +
geom_boxplot() 
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b750, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 11 10.83 0.484 0.487
## Residuals 945 21143 22.37
## 23 observations deleted due to missingness5.1.4 Gentrified CT | 2011
5.1.4.1 Bike lane length
Measuring bike lane length change within CT/buffers (in meters) between 2011 and 2016 (and not between 2006 and 2011)
5.1.4.1.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(x=Bike_lane_diff.2011.2016ct, y=gentrified_2011_2006)) +
geom_boxplot() 
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016ct, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016ct ~ gentrified_2011_2006, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 691401 691401 0.805 0.37
## Residuals 886 760756987 858642
## 82 observations deleted due to missingness5.1.4.1.2 Buffers 250m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(x=Bike_lane_diff.2011.2016b250, y=gentrified_2011_2006)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b250, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b250 ~ gentrified_2011_2006, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 3.867e+07 38666795 17.08 3.92e-05 ***
## Residuals 886 2.005e+09 2263514
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 82 observations deleted due to missingness5.1.4.1.3 Buffers 500m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(x=Bike_lane_diff.2011.2016b500, y=gentrified_2011_2006)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b500, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b500 ~ gentrified_2011_2006, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 1.625e+08 162483870 34.41 6.3e-09 ***
## Residuals 886 4.184e+09 4722050
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 82 observations deleted due to missingness5.1.4.1.4 Buffers 750m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(x=Bike_lane_diff.2011.2016b750, y=gentrified_2011_2006)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b750, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b750 ~ gentrified_2011_2006, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 5.376e+08 537556297 61.72 1.14e-14 ***
## Residuals 886 7.717e+09 8709779
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 82 observations deleted due to missingness5.1.4.2 Canopy change
5.1.4.2.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(x=pct_esp_vert_diff_2011.2017ct, y=gentrified_2011_2006)) +
geom_boxplot() 
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017ct, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017ct ~ gentrified_2011_2006, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 6 5.501 0.193 0.661
## Residuals 886 25317 28.575
## 82 observations deleted due to missingness5.1.4.2.2 Buffer 250m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(x=pct_esp_vert_diff_2011.2017b250, y=gentrified_2011_2006)) +
geom_boxplot() 
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b250, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b250 ~ gentrified_2011_2006, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 1 1.239 0.059 0.809
## Residuals 886 18707 21.114
## 82 observations deleted due to missingness5.1.4.2.3 Buffer 500m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(x=pct_esp_vert_diff_2011.2017b500, y=gentrified_2011_2006)) +
geom_boxplot() 
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b500, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b500 ~ gentrified_2011_2006, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 6 5.608 0.283 0.595
## Residuals 886 17533 19.789
## 82 observations deleted due to missingness5.1.4.2.4 Buffer 750m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2011_2006), aes(x=pct_esp_vert_diff_2011.2017b750, y=gentrified_2011_2006)) +
geom_boxplot() 
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b750, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b750 ~ gentrified_2011_2006, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 13 12.67 0.67 0.413
## Residuals 886 16750 18.91
## 82 observations deleted due to missingness5.1.5 Gentrified CT | 2006
5.1.5.1 Bike lane length
Measuring bike lane length change within CT/buffers (in meters) between 2011 and 2016 (and not between 2001 and 2006)
5.1.5.1.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df), gentrified_2006_2001), aes(x=Bike_lane_diff.2011.2016ct, y=gentrified_2006_2001)) +
geom_boxplot() 
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016ct, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016ct ~ gentrified_2006_2001, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 1629828 1629828 3.598 0.0582 .
## Residuals 802 363318022 453015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 166 observations deleted due to missingness5.1.5.1.2 Buffers 250m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2006_2001), aes(x=Bike_lane_diff.2011.2016b250, y=gentrified_2006_2001)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b250, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b250 ~ gentrified_2006_2001, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 5.52e+07 55201554 40.24 3.74e-10 ***
## Residuals 802 1.10e+09 1371697
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 166 observations deleted due to missingness5.1.5.1.3 Buffers 500m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2006_2001), aes(x=Bike_lane_diff.2011.2016b500, y=gentrified_2006_2001)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b500, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b500 ~ gentrified_2006_2001, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 2.423e+08 242253012 71.08 <2e-16 ***
## Residuals 802 2.733e+09 3408209
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 166 observations deleted due to missingness5.1.5.1.4 Buffers 750m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2006_2001), aes(x=Bike_lane_diff.2011.2016b750, y=gentrified_2006_2001)) +
geom_boxplot()
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b750, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b750 ~ gentrified_2006_2001, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 7.300e+08 729955328 105.4 <2e-16 ***
## Residuals 802 5.555e+09 6926433
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 166 observations deleted due to missingness5.1.5.2 Canopy change
5.1.5.2.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df), gentrified_2006_2001), aes(x=pct_esp_vert_diff_2011.2017ct, y=gentrified_2006_2001)) +
geom_boxplot() 
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017ct, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017ct ~ gentrified_2006_2001, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 11 11.04 0.431 0.512
## Residuals 802 20533 25.60
## 166 observations deleted due to missingness5.1.5.2.2 Buffer 250m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2006_2001), aes(x=pct_esp_vert_diff_2011.2017b250, y=gentrified_2006_2001)) +
geom_boxplot() 
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b250, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b250 ~ gentrified_2006_2001, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 14 13.97 0.718 0.397
## Residuals 802 15612 19.47
## 166 observations deleted due to missingness5.1.5.2.3 Buffer 500m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2006_2001), aes(x=pct_esp_vert_diff_2011.2017b500, y=gentrified_2006_2001)) +
geom_boxplot() 
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b500, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b500 ~ gentrified_2006_2001, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 7 7.197 0.394 0.531
## Residuals 802 14667 18.288
## 166 observations deleted due to missingness5.1.5.2.4 Buffer 750m
ggplot(drop_na(units::drop_units(bei_df), gentrified_2006_2001), aes(x=pct_esp_vert_diff_2011.2017b750, y=gentrified_2006_2001)) +
geom_boxplot() 
bei_df %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b750, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b750 ~ gentrified_2006_2001, data = units::drop_units(bei_df))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 1 0.64 0.036 0.849
## Residuals 802 14179 17.68
## 166 observations deleted due to missingness5.2 Association between outcomes and independant variables | INTERACT study area
# keep only interact CT
bei_df_aoi <- filter(bei_df, interact_aoi)5.2.1 Pampalon | material vs. BEI
5.2.1.1 Bike lane length
Measuring bike lane length change within CT/buffers (in meters) between 2011 and 2016
5.2.1.1.1 Census tract level
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.2011.2016ct, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016ct, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.142085 -0.024870 0.001266 0.026881 0.137334
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.543e-03 1.605e-03 0.961 0.3368
## Bike_lane_diff.2011.2016ct -3.559e-06 1.462e-06 -2.434 0.0152 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03979 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.008529, Adjusted R-squared: 0.00709
## F-statistic: 5.927 on 1 and 689 DF, p-value: 0.015175.2.1.1.2 Buffers 250m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.2011.2016b250, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016b250, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.143165 -0.025609 0.002509 0.026293 0.136254
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.623e-03 1.716e-03 1.528 0.12694
## Bike_lane_diff.2011.2016b250 -2.651e-06 9.069e-07 -2.924 0.00357 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03972 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.01225, Adjusted R-squared: 0.01082
## F-statistic: 8.547 on 1 and 689 DF, p-value: 0.0035745.2.1.1.3 Buffers 500m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.2011.2016b500, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016b500, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.144252 -0.025817 0.001971 0.026545 0.135167
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.710e-03 1.819e-03 2.039 0.041793 *
## Bike_lane_diff.2011.2016b500 -2.151e-06 6.312e-07 -3.407 0.000694 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03963 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.01657, Adjusted R-squared: 0.01515
## F-statistic: 11.61 on 1 and 689 DF, p-value: 0.00069385.2.1.1.4 Buffers 750m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.2011.2016b750, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.2011.2016b750, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14482 -0.02576 0.00209 0.02703 0.13460
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.274e-03 1.911e-03 2.236 0.025643 *
## Bike_lane_diff.2011.2016b750 -1.594e-06 4.645e-07 -3.432 0.000634 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03962 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.01681, Adjusted R-squared: 0.01538
## F-statistic: 11.78 on 1 and 689 DF, p-value: 0.00063415.2.1.2 Bike lane / street
Measuring bike lane length change, normalized by length of streets within CT/buffers (in meters)
5.2.1.2.1 Census tract level
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.street.2011.2016ct, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016ct,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.141912 -0.025419 0.001723 0.026550 0.137507
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0013695 0.0017044 0.804 0.422
## Bike_lane_diff.by.street.2011.2016ct -0.0003076 0.0002114 -1.455 0.146
##
## Residual standard error: 0.0399 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.003064, Adjusted R-squared: 0.001617
## F-statistic: 2.118 on 1 and 689 DF, p-value: 0.14615.2.1.2.2 Buffer 250m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.street.2011.2016b250, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016b250,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.143191 -0.025638 0.002861 0.026222 0.136228
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0026491 0.0017911 1.479 0.1396
## Bike_lane_diff.by.street.2011.2016b250 -0.0006974 0.0002775 -2.513 0.0122 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03978 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.009083, Adjusted R-squared: 0.007645
## F-statistic: 6.316 on 1 and 689 DF, p-value: 0.012195.2.1.2.3 Buffer 500m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.street.2011.2016b500, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016b500,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.143997 -0.026054 0.002716 0.026615 0.135422
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0034549 0.0018726 1.845 0.06547
## Bike_lane_diff.by.street.2011.2016b500 -0.0009344 0.0003216 -2.905 0.00379
##
## (Intercept) .
## Bike_lane_diff.by.street.2011.2016b500 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03972 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.0121, Adjusted R-squared: 0.01067
## F-statistic: 8.44 on 1 and 689 DF, p-value: 0.0037875.2.1.2.4 Buffer 750m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.street.2011.2016b750, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.street.2011.2016b750,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.144234 -0.025710 0.003126 0.027081 0.135185
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0036914 0.0019482 1.895 0.05855
## Bike_lane_diff.by.street.2011.2016b750 -0.0010041 0.0003577 -2.807 0.00514
##
## (Intercept) .
## Bike_lane_diff.by.street.2011.2016b750 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03973 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.01131, Adjusted R-squared: 0.009871
## F-statistic: 7.879 on 1 and 689 DF, p-value: 0.0051435.2.1.3 Bike lane / area
Measuring bike lane length change, normalized by area within CT/buffers (in meters)
5.2.1.3.1 Census tract level
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.area.2011.2016ct, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016ct,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.141512 -0.025490 0.001817 0.026904 0.137906
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0009702 0.0016937 0.573 0.567
## Bike_lane_diff.by.area.2011.2016ct -0.0012264 0.0012601 -0.973 0.331
##
## Residual standard error: 0.03993 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.001373, Adjusted R-squared: -7.641e-05
## F-statistic: 0.9473 on 1 and 689 DF, p-value: 0.33085.2.1.3.2 Buffer 250m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.area.2011.2016b250, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016b250,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.143015 -0.025663 0.002708 0.026574 0.136404
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.002473 0.001764 1.402 0.1613
## Bike_lane_diff.by.area.2011.2016b250 -0.003997 0.001621 -2.466 0.0139 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03979 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.008746, Adjusted R-squared: 0.007308
## F-statistic: 6.079 on 1 and 689 DF, p-value: 0.013925.2.1.3.3 Buffer 500m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.area.2011.2016b500, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016b500,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14380 -0.02586 0.00240 0.02701 0.13561
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003263 0.001829 1.784 0.0749 .
## Bike_lane_diff.by.area.2011.2016b500 -0.005608 0.001914 -2.931 0.0035 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03971 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.01231, Adjusted R-squared: 0.01088
## F-statistic: 8.588 on 1 and 689 DF, p-value: 0.0034965.2.1.3.4 Buffer 750m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.area.2011.2016b750, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ Bike_lane_diff.by.area.2011.2016b750,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14410 -0.02554 0.00260 0.02712 0.13532
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003555 0.001881 1.890 0.05924 .
## Bike_lane_diff.by.area.2011.2016b750 -0.006271 0.002122 -2.955 0.00323 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03971 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.01252, Adjusted R-squared: 0.01108
## F-statistic: 8.734 on 1 and 689 DF, p-value: 0.003235.2.1.4 Canopy change
Measuring canopy (i.e. greenness) ratio change within CT/buffer between 2011 and 2017 (in %)
5.2.1.4.1 Census tract level
ggplot(units::drop_units(bei_df_aoi), aes(x=pct_esp_vert_diff_2011.2017ct, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017ct,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.141301 -0.026512 0.001512 0.027090 0.141804
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0015075 0.0023928 0.630 0.529
## pct_esp_vert_diff_2011.2017ct -0.0003044 0.0004443 -0.685 0.494
##
## Residual standard error: 0.03995 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.0006807, Adjusted R-squared: -0.0007697
## F-statistic: 0.4693 on 1 and 689 DF, p-value: 0.49355.2.1.4.2 Buffer 250m
ggplot(units::drop_units(bei_df_aoi), aes(x=pct_esp_vert_diff_2011.2017b250, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017b250,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.140912 -0.026055 0.001444 0.026847 0.139639
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0040645 0.0025479 1.595 0.1111
## pct_esp_vert_diff_2011.2017b250 -0.0009673 0.0005180 -1.867 0.0623 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03986 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.005035, Adjusted R-squared: 0.003591
## F-statistic: 3.486 on 1 and 689 DF, p-value: 0.06235.2.1.4.3 Buffer 500m
ggplot(units::drop_units(bei_df_aoi), aes(x=pct_esp_vert_diff_2011.2017b500, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017b500,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.140252 -0.025600 0.001226 0.026962 0.139502
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0040793 0.0026421 1.544 0.1231
## pct_esp_vert_diff_2011.2017b500 -0.0009862 0.0005559 -1.774 0.0765 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03987 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.004547, Adjusted R-squared: 0.003103
## F-statistic: 3.147 on 1 and 689 DF, p-value: 0.076495.2.1.4.4 Buffer 750m
ggplot(units::drop_units(bei_df_aoi), aes(x=pct_esp_vert_diff_2011.2017b750, y=wSCOREMAT.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCOREMAT.2016 ~ pct_esp_vert_diff_2011.2017b750,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.140111 -0.026318 0.001616 0.026562 0.139369
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0049083 0.0026849 1.828 0.0680 .
## pct_esp_vert_diff_2011.2017b750 -0.0012163 0.0005777 -2.106 0.0356 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03983 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.006394, Adjusted R-squared: 0.004952
## F-statistic: 4.434 on 1 and 689 DF, p-value: 0.035595.2.2 Pampalon | social vs. BEI
5.2.2.1 Bike lane length
Measuring bike lane length change within CT/buffers (in meters) between 2011 and 2016
5.2.2.1.1 Census tract level
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.2011.2016ct, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.2011.2016ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.2011.2016ct, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.077632 -0.020726 0.002851 0.023379 0.088820
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.415e-02 1.173e-03 20.593 <2e-16 ***
## Bike_lane_diff.2011.2016ct -2.085e-06 1.068e-06 -1.953 0.0513 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02907 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.005504, Adjusted R-squared: 0.00406
## F-statistic: 3.813 on 1 and 689 DF, p-value: 0.051265.2.2.1.2 Buffers 250m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.2011.2016b250, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.2011.2016b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.2011.2016b250, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.076323 -0.020636 0.003626 0.023255 0.090129
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.284e-02 1.259e-03 18.142 <2e-16 ***
## Bike_lane_diff.2011.2016b250 6.078e-07 6.651e-07 0.914 0.361
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02913 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.001211, Adjusted R-squared: -0.000239
## F-statistic: 0.8351 on 1 and 689 DF, p-value: 0.36115.2.2.1.3 Buffers 500m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.2011.2016b500, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.2011.2016b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.2011.2016b500, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.075316 -0.020362 0.003956 0.022246 0.092087
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.088e-02 1.327e-03 15.737 < 2e-16 ***
## Bike_lane_diff.2011.2016b500 1.553e-06 4.605e-07 3.372 0.000788 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02891 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.01624, Adjusted R-squared: 0.01481
## F-statistic: 11.37 on 1 and 689 DF, p-value: 0.00078775.2.2.1.4 Buffers 750m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.2011.2016b750, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.2011.2016b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.2011.2016b750, data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07989 -0.02017 0.00368 0.02173 0.09403
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.894e-02 1.379e-03 13.738 < 2e-16 ***
## Bike_lane_diff.2011.2016b750 1.758e-06 3.350e-07 5.247 2.06e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02858 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.03843, Adjusted R-squared: 0.03703
## F-statistic: 27.54 on 1 and 689 DF, p-value: 2.055e-075.2.2.2 Bike lane / street
Measuring bike lane length change, normalized by length of streets within CT/buffers (in meters)
5.2.2.2.1 Census tract level
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.street.2011.2016ct, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016ct,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073622 -0.020689 0.003123 0.020500 0.092831
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0201386 0.0012150 16.575 < 2e-16 ***
## Bike_lane_diff.by.street.2011.2016ct 0.0008854 0.0001507 5.875 6.57e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02844 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.04771, Adjusted R-squared: 0.04633
## F-statistic: 34.52 on 1 and 689 DF, p-value: 6.572e-095.2.2.2.2 Buffer 250m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.street.2011.2016b250, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016b250,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073366 -0.020023 0.003232 0.020039 0.094869
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0180999 0.0012572 14.40 < 2e-16
## Bike_lane_diff.by.street.2011.2016b250 0.0015309 0.0001948 7.86 1.48e-14
##
## (Intercept) ***
## Bike_lane_diff.by.street.2011.2016b250 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02792 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.08229, Adjusted R-squared: 0.08096
## F-statistic: 61.78 on 1 and 689 DF, p-value: 1.479e-145.2.2.2.3 Buffer 500m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.street.2011.2016b500, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016b500,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.079373 -0.019433 0.002286 0.019687 0.096237
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0167316 0.0013054 12.817 <2e-16
## Bike_lane_diff.by.street.2011.2016b500 0.0019347 0.0002242 8.629 <2e-16
##
## (Intercept) ***
## Bike_lane_diff.by.street.2011.2016b500 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02769 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.09753, Adjusted R-squared: 0.09622
## F-statistic: 74.46 on 1 and 689 DF, p-value: < 2.2e-165.2.2.2.4 Buffer 750m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.street.2011.2016b750, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.street.2011.2016b750,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.082775 -0.019116 0.002334 0.019230 0.097355
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0156136 0.0013498 11.567 <2e-16
## Bike_lane_diff.by.street.2011.2016b750 0.0022619 0.0002478 9.126 <2e-16
##
## (Intercept) ***
## Bike_lane_diff.by.street.2011.2016b750 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02753 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.1079, Adjusted R-squared: 0.1066
## F-statistic: 83.29 on 1 and 689 DF, p-value: < 2.2e-165.2.2.3 Bike lane / area
Measuring bike lane length change, normalized by area within CT/buffers (in meters)
5.2.2.3.1 Census tract level
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.area.2011.2016ct, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016ct,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073236 -0.020515 0.002866 0.020518 0.093217
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0197525 0.0011959 16.517 < 2e-16 ***
## Bike_lane_diff.by.area.2011.2016ct 0.0061132 0.0008897 6.871 1.43e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0282 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.06413, Adjusted R-squared: 0.06277
## F-statistic: 47.21 on 1 and 689 DF, p-value: 1.428e-115.2.2.3.2 Buffer 250m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.area.2011.2016b250, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016b250,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.071159 -0.019660 0.002669 0.019434 0.095294
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.017675 0.001220 14.483 <2e-16 ***
## Bike_lane_diff.by.area.2011.2016b250 0.010228 0.001122 9.117 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02753 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.1077, Adjusted R-squared: 0.1064
## F-statistic: 83.12 on 1 and 689 DF, p-value: < 2.2e-165.2.2.3.3 Buffer 500m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.area.2011.2016b500, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016b500,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.073323 -0.018545 0.000951 0.019280 0.096782
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.016187 0.001251 12.94 <2e-16 ***
## Bike_lane_diff.by.area.2011.2016b500 0.013362 0.001309 10.21 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02716 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.1314, Adjusted R-squared: 0.1301
## F-statistic: 104.2 on 1 and 689 DF, p-value: < 2.2e-165.2.2.3.4 Buffer 750m
ggplot(units::drop_units(bei_df_aoi), aes(x=Bike_lane_diff.by.area.2011.2016b750, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ Bike_lane_diff.by.area.2011.2016b750,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.072996 -0.018383 0.001469 0.018820 0.097856
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.015113 0.001275 11.85 <2e-16 ***
## Bike_lane_diff.by.area.2011.2016b750 0.015657 0.001439 10.88 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02692 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.1467, Adjusted R-squared: 0.1455
## F-statistic: 118.4 on 1 and 689 DF, p-value: < 2.2e-165.2.2.4 Canopy change
Measuring canopy (i.e. greenness) ratio change within CT/buffer between 2011 and 2017 (in %)
5.2.2.4.1 Census tract level
ggplot(units::drop_units(bei_df_aoi), aes(x=pct_esp_vert_diff_2011.2017ct, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017ct, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017ct,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.074002 -0.020739 0.002973 0.021949 0.092434
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0164107 0.0017116 9.588 < 2e-16 ***
## pct_esp_vert_diff_2011.2017ct 0.0016766 0.0003178 5.276 1.77e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02857 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.03883, Adjusted R-squared: 0.03743
## F-statistic: 27.83 on 1 and 689 DF, p-value: 1.772e-075.2.2.4.2 Buffer 250m
ggplot(units::drop_units(bei_df_aoi), aes(x=pct_esp_vert_diff_2011.2017b250, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017b250, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017b250,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.088141 -0.019980 0.003517 0.021584 0.089886
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0143620 0.0018133 7.920 9.50e-15 ***
## pct_esp_vert_diff_2011.2017b250 0.0022831 0.0003687 6.193 1.02e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02837 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.05272, Adjusted R-squared: 0.05135
## F-statistic: 38.35 on 1 and 689 DF, p-value: 1.017e-095.2.2.4.3 Buffer 500m
ggplot(units::drop_units(bei_df_aoi), aes(x=pct_esp_vert_diff_2011.2017b500, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017b500, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017b500,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.088327 -0.020377 0.003395 0.021423 0.088250
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0137482 0.0018787 7.318 7.02e-13 ***
## pct_esp_vert_diff_2011.2017b500 0.0024765 0.0003953 6.265 6.56e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02835 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.0539, Adjusted R-squared: 0.05253
## F-statistic: 39.25 on 1 and 689 DF, p-value: 6.556e-105.2.2.4.4 Buffer 750m
ggplot(units::drop_units(bei_df_aoi), aes(x=pct_esp_vert_diff_2011.2017b750, y=wSCORESOC.2016)) +
geom_point() +
geom_smooth(method=lm)
res.lm <- lm(wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017b750, data = units::drop_units(bei_df_aoi))
summary(res.lm)##
## Call:
## lm(formula = wSCORESOC.2016 ~ pct_esp_vert_diff_2011.2017b750,
## data = units::drop_units(bei_df_aoi))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.081951 -0.019840 0.003141 0.021681 0.088200
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0137891 0.0019140 7.204 1.53e-12 ***
## pct_esp_vert_diff_2011.2017b750 0.0025011 0.0004118 6.074 2.07e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0284 on 689 degrees of freedom
## (14 observations deleted due to missingness)
## Multiple R-squared: 0.05082, Adjusted R-squared: 0.04944
## F-statistic: 36.89 on 1 and 689 DF, p-value: 2.066e-095.2.3 Gentrified CT | 2016
5.2.3.1 Bike lane length
Measuring bike lane length change within CT/buffers (in meters) between 2011 and 2016
5.2.3.1.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=Bike_lane_diff.2011.2016ct, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016ct, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 1115825 1115825 1.038 0.309
## Residuals 688 739651503 1075075
## 15 observations deleted due to missingness5.2.3.1.2 Buffers 250m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=Bike_lane_diff.2011.2016b250, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b250, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 4.340e+06 4340160 1.56 0.212
## Residuals 688 1.914e+09 2781403
## 15 observations deleted due to missingness5.2.3.1.3 Buffers 500m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=Bike_lane_diff.2011.2016b500, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b500, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 4.732e+07 47317317 8.356 0.00396 **
## Residuals 688 3.896e+09 5662417
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness5.2.3.1.4 Buffers 750m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=Bike_lane_diff.2011.2016b750, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b750, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 1.527e+08 152740935 14.75 0.000134 ***
## Residuals 688 7.125e+09 10356102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness5.2.3.2 Bike lane / street
Measuring bike lane length change, normalized by length of streets within CT/buffers (in meters)
5.2.3.2.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=Bike_lane_diff.by.street.2011.2016ct, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.street.2011.2016ct, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.street.2011.2016ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.street.2011.2016ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 843 843.4 16.69 4.92e-05 ***
## Residuals 688 34767 50.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness5.2.3.2.2 Buffers 250m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=Bike_lane_diff.by.street.2011.2016b250, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.street.2011.2016b250, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.street.2011.2016b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.street.2011.2016b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 1124 1123.9 39.8 5.03e-10 ***
## Residuals 688 19426 28.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness5.2.3.2.3 Buffers 500m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=Bike_lane_diff.by.street.2011.2016b500, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.street.2011.2016b500, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.street.2011.2016b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.street.2011.2016b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 965 965.1 46.5 2.01e-11 ***
## Residuals 688 14280 20.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness5.2.3.2.4 Buffers 750m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=Bike_lane_diff.by.street.2011.2016b750, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.street.2011.2016b750, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.street.2011.2016b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.street.2011.2016b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 759 758.6 45.14 3.85e-11 ***
## Residuals 688 11563 16.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness5.2.3.3 Bike lane / area
Measuring bike lane length change, normalized by area within CT/buffers (in meters)
5.2.3.3.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=Bike_lane_diff.by.area.2011.2016ct, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.area.2011.2016ct, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.area.2011.2016ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.area.2011.2016ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 34.5 34.48 24.46 9.52e-07 ***
## Residuals 688 969.5 1.41
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness5.2.3.3.2 Buffers 250m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=Bike_lane_diff.by.area.2011.2016b250, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.area.2011.2016b250, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.area.2011.2016b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.area.2011.2016b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 41.2 41.17 50.48 3.01e-12 ***
## Residuals 688 561.1 0.82
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness5.2.3.3.3 Buffers 500m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=Bike_lane_diff.by.area.2011.2016b500, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.area.2011.2016b500, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.area.2011.2016b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.area.2011.2016b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 35.4 35.39 61.66 1.57e-14 ***
## Residuals 688 394.9 0.57
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness5.2.3.3.4 Buffers 750m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=Bike_lane_diff.by.area.2011.2016b750, y=gentrified_2016_2011)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.by.area.2011.2016b750, na.rm = TRUE),
sd = sd(Bike_lane_diff.by.area.2011.2016b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.by.area.2011.2016b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 28.1 28.072 60.08 3.28e-14 ***
## Residuals 688 321.5 0.467
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness5.2.3.4 Canopy change
5.2.3.4.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=pct_esp_vert_diff_2011.2017ct, y=gentrified_2016_2011)) +
geom_boxplot() 
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017ct, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017ct ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 97 96.85 8.355 0.00397 **
## Residuals 688 7975 11.59
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness5.2.3.4.2 Buffer 250m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=pct_esp_vert_diff_2011.2017b250, y=gentrified_2016_2011)) +
geom_boxplot() 
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b250, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b250 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 57 56.50 6.639 0.0102 *
## Residuals 688 5855 8.51
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness5.2.3.4.3 Buffer 500m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=pct_esp_vert_diff_2011.2017b500, y=gentrified_2016_2011)) +
geom_boxplot() 
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b500, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b500 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 59 58.84 7.967 0.0049 **
## Residuals 688 5081 7.39
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness5.2.3.4.4 Buffer 750m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2016_2011), aes(x=pct_esp_vert_diff_2011.2017b750, y=gentrified_2016_2011)) +
geom_boxplot() 
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2016_2011) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b750, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b750 ~ gentrified_2016_2011, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2016_2011 1 53 53.32 7.808 0.00535 **
## Residuals 688 4699 6.83
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 15 observations deleted due to missingness5.2.4 Gentrified CT | 2011
5.2.4.1 Bike lane length
Measuring bike lane length change within CT/buffers (in meters) between 2011 and 2016 (and not between 2006 and 2011)
5.2.4.1.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(x=Bike_lane_diff.2011.2016ct, y=gentrified_2011_2006)) +
geom_boxplot() 
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016ct, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016ct ~ gentrified_2011_2006, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 227980 227980 0.208 0.648
## Residuals 674 738566819 1095796
## 29 observations deleted due to missingness5.2.4.1.2 Buffers 250m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(x=Bike_lane_diff.2011.2016b250, y=gentrified_2011_2006)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b250, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b250 ~ gentrified_2011_2006, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 1.097e+07 10973598 3.898 0.0487 *
## Residuals 674 1.897e+09 2815091
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 29 observations deleted due to missingness5.2.4.1.3 Buffers 500m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(x=Bike_lane_diff.2011.2016b500, y=gentrified_2011_2006)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b500, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b500 ~ gentrified_2011_2006, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 6.005e+07 60051961 10.51 0.00125 **
## Residuals 674 3.852e+09 5715401
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 29 observations deleted due to missingness5.2.4.1.4 Buffers 750m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(x=Bike_lane_diff.2011.2016b750, y=gentrified_2011_2006)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b750, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b750 ~ gentrified_2011_2006, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 2.429e+08 242939736 23.52 1.54e-06 ***
## Residuals 674 6.962e+09 10329115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 29 observations deleted due to missingness5.2.4.2 Canopy change
5.2.4.2.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(x=pct_esp_vert_diff_2011.2017ct, y=gentrified_2011_2006)) +
geom_boxplot() 
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017ct, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017ct ~ gentrified_2011_2006, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 62 61.93 5.247 0.0223 *
## Residuals 674 7955 11.80
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 29 observations deleted due to missingness5.2.4.2.2 Buffer 250m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(x=pct_esp_vert_diff_2011.2017b250, y=gentrified_2011_2006)) +
geom_boxplot() 
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b250, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b250 ~ gentrified_2011_2006, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 71 71.25 8.247 0.00421 **
## Residuals 674 5823 8.64
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 29 observations deleted due to missingness5.2.4.2.3 Buffer 500m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(x=pct_esp_vert_diff_2011.2017b500, y=gentrified_2011_2006)) +
geom_boxplot() 
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b500, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b500 ~ gentrified_2011_2006, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 61 61.45 8.204 0.00431 **
## Residuals 674 5048 7.49
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 29 observations deleted due to missingness5.2.4.2.4 Buffer 750m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2011_2006), aes(x=pct_esp_vert_diff_2011.2017b750, y=gentrified_2011_2006)) +
geom_boxplot() 
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2011_2006) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b750, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b750 ~ gentrified_2011_2006, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2011_2006 1 55 54.67 7.887 0.00512 **
## Residuals 674 4672 6.93
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 29 observations deleted due to missingness5.2.5 Gentrified CT | 2006
5.2.5.1 Bike lane length
Measuring bike lane length change within CT/buffers (in meters) between 2011 and 2016 (and not between 2001 and 2006)
5.2.5.1.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2006_2001), aes(x=Bike_lane_diff.2011.2016ct, y=gentrified_2006_2001)) +
geom_boxplot() 
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016ct, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016ct ~ gentrified_2006_2001, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 27515 27515 0.05 0.824
## Residuals 634 351091051 553771
## 69 observations deleted due to missingness5.2.5.1.2 Buffers 250m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2006_2001), aes(x=Bike_lane_diff.2011.2016b250, y=gentrified_2006_2001)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b250, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b250 ~ gentrified_2006_2001, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 2.192e+07 21920663 13.42 0.00027 ***
## Residuals 634 1.036e+09 1633304
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 69 observations deleted due to missingness5.2.5.1.3 Buffers 500m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2006_2001), aes(x=Bike_lane_diff.2011.2016b500, y=gentrified_2006_2001)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b500, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b500 ~ gentrified_2006_2001, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 1.139e+08 113920313 28.58 1.25e-07 ***
## Residuals 634 2.527e+09 3985549
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 69 observations deleted due to missingness5.2.5.1.4 Buffers 750m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2006_2001), aes(x=Bike_lane_diff.2011.2016b750, y=gentrified_2006_2001)) +
geom_boxplot()
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(Bike_lane_diff.2011.2016b750, na.rm = TRUE),
sd = sd(Bike_lane_diff.2011.2016b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(Bike_lane_diff.2011.2016b750 ~ gentrified_2006_2001, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 3.754e+08 375368237 46.86 1.8e-11 ***
## Residuals 634 5.079e+09 8010555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 69 observations deleted due to missingness5.2.5.2 Canopy change
5.2.5.2.1 Census tract level
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2006_2001), aes(x=pct_esp_vert_diff_2011.2017ct, y=gentrified_2006_2001)) +
geom_boxplot() 
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017ct, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017ct, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017ct ~ gentrified_2006_2001, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 173 172.53 16.48 5.52e-05 ***
## Residuals 634 6635 10.47
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 69 observations deleted due to missingness5.2.5.2.2 Buffer 250m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2006_2001), aes(x=pct_esp_vert_diff_2011.2017b250, y=gentrified_2006_2001)) +
geom_boxplot() 
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b250, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b250, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b250 ~ gentrified_2006_2001, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 170 170.16 22.26 2.93e-06 ***
## Residuals 634 4847 7.64
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 69 observations deleted due to missingness5.2.5.2.3 Buffer 500m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2006_2001), aes(x=pct_esp_vert_diff_2011.2017b500, y=gentrified_2006_2001)) +
geom_boxplot() 
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b500, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b500, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b500 ~ gentrified_2006_2001, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 150 150.20 22.59 2.48e-06 ***
## Residuals 634 4216 6.65
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 69 observations deleted due to missingness5.2.5.2.4 Buffer 750m
ggplot(drop_na(units::drop_units(bei_df_aoi), gentrified_2006_2001), aes(x=pct_esp_vert_diff_2011.2017b750, y=gentrified_2006_2001)) +
geom_boxplot() 
bei_df_aoi %>%
units::drop_units() %>%
drop_na() %>%
group_by(gentrified_2006_2001) %>%
summarise(
count = n(),
mean = mean(pct_esp_vert_diff_2011.2017b750, na.rm = TRUE),
sd = sd(pct_esp_vert_diff_2011.2017b750, na.rm = TRUE)
)# Compute the analysis of variance
res.aov <- aov(pct_esp_vert_diff_2011.2017b750 ~ gentrified_2006_2001, data = units::drop_units(bei_df_aoi))
# Summary of the analysis
summary(res.aov)## Df Sum Sq Mean Sq F value Pr(>F)
## gentrified_2006_2001 1 121 120.76 19.42 1.23e-05 ***
## Residuals 634 3942 6.22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 69 observations deleted due to missingness